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In this dissertation, I report findings of a case study I conducted in Giles County, Virginia, my hometown, to better understand the way land impacts rural digital literacy sponsorship. With the help of 13 participants, I studied the ways land impacted their access to digital infrastructures and internet access and presented the following questions: how does land impeded access and support access to broadband infrastructure in Giles County? How does land impact what digital literacies residents in Giles County pursue, and how do they navigate the county's "dead zones" I begin the study by diving into the status of internet in Giles County, and I review the failed attempt made by legislators to provide "universal broadband" to the entire state of Virginia. Next, I review the literature on Deep Mapping Analysis and Literacy Networks to provide context as to how I am using land as a framework for studying rural digital literacy sponsorship. Then, the first analysis chapter uses Deep Mapping Analysis to discuss maps reflecting the status of internet access within the county, and then discusses how these maps help support the experiences of participants in the first round of interviews. The second findings chapter focuses on the Literacy Networks created by participants Veronica, Libby, Makayla, and Heather and explores how their networks show the connections between land and infrastructures as rural digital literacy sponsors and what that teaches us about rural access in general. Finally, I conclude with three implications from these analysis chapters that encourage scholars, community scholars, and legislators alike to pay more attention to the role land plays in internet access and rural digital literacy sponsorship. Most importantly, this work calls for legislators to consider what their part is in helping their rural constituents receive the internet access they need to survive without implicating those who cannot afford it or harming the land where they live. Doctor of Philosophy In this dissertation, I report findings of a case study I conducted in Giles County, Virginia, my hometown, to better understand how land sponsors rural digital literacies. With the help of 13 participants, I studied the ways land impacted their access to digital infrastructures and internet access via the following questions: How does land hinder or support access to broadband infrastructure in Giles County? How does land impact how residents interact with technology while living in Giles County? And, how do they navigate the county's "dead zones?" I first describe the status of internet in Giles County by reviewing the failed attempts made by legislators to provide "universal broadband" to the entire state of Virginia. Next, I review the literature on literacy sponsorship and rural literacies to provide context as to how I am using land as a framework for studying rural digital literacy sponsorship. The first analysis chapter uses maps to discuss the status of internet access within the county, and then I explain how these maps provide supporting information for the experiences of participants in the first round of interviews. The second findings chapter uses drawings from my participants that depict their rural digital literacy sponsorship in a network form, which was followed by a second interview to discuss the connections between land, infrastructure and rural digital literacy illustrated in their drawing. Finally, I conclude with three implications from these analysis chapters that encourage scholars, community scholars, and legislators alike to pay more attention to the role land plays in internet access and rural digital literacy sponsorship. Most importantly, this work calls for legislators to consider what their part is in helping their rural constituents receive the internet access they need without implicating those who cannot afford it or harming the land where they live.

With the advent of electric vehicles (EVs) and the proliferation of vehicle technologies like drive-by-wire and autonomous driving, advanced communication protocols to connect vehicles and the infrastructure have been proposed. However, practical large-scale deployments have been hindered due to caveats such as hardware, and infrastructure demands — including the security of vehicles, given their ubiquitous nature and direct correlation to human safety. As part of this thesis, we look at deploying a practical solution to adopt a secure large-scale vehicle-to-everything (V2X) communication architecture. Then, we also try to analyze and detect vulnerabilities in vehicle-to-grid communication for electric vehicles. In the first work, we analyze, build a proof of concept and evaluate the use of commercial off-the-shelf (COTS) smartphones as secure cellular-vehicle-to-everything (CV2X) radios. Here, we study the various possible network topologies considering the long-term evolution (LTE) technology with necessary latency requirements considering security and the associated overhead. We further simulate the proposed method by considering real-world scalability for practical deployment. In the second work, we analyze the ISO15118 standard for EV-to-electric grid communication involving high levels of energy exchange. We develop a grammatical fuzzing architecture to assess and evaluate the implementation of the standard on a road-deployed vehicle to detect security vulnerabilities and shortcomings. Master of Science The technology around vehicles and the transportation infrastructure has immensely advanced in the last few decades. Today we have advanced technologies like driver assistance, automated driving, and access to multimedia within our vehicles. And deploying such technologies has only been possible due to advancements in the electronics embedded in the vehicles and surrounding infrastructure. Opportunely, we can further improve the technologies to include numerous safety features by connecting vehicles and infrastructure via communication networks. However, this poses immense challenges regarding the scaling of communication infrastructure for the timely exchange of data and its security. But, given the proliferation of cellular technology, the ubiquitous nature of smartphones, and their capabilities, we propose and evaluate the idea of using commercial off-the-shelf (COTS) smartphones to connect vehicles and the infrastructure to exchange data securely. The first work of this thesis details the analysis and evaluation of the system and the network for a secure COTS-based cellular-vehicle-to-everything architecture, including a proof of concept hardware implementation and additional simulations. Additionally, in light of climate policies and cleaner transportation alternatives, we are moving from gasoline-based internal combustion engines to electric vehicles, requiring the transfer of extended amounts of electric energy from the electric grid to the batteries in the vehicles. In light of the same, ISO 15118 standard was developed to reduce repetitive efforts and standardize the communication and exchange of this energy. But as with any new technology, especially involving communication, new attack vectors for malicious entities open up. Therefore, we study this new standard and develop a novel fuzzing architecture to test the implementation of the standard on deployed real-world vehicles for security vulnerabilities and robustness. Again, as this is a nascent technology and standard, a fuzzing approach would accelerate the detection of edge cases and threats before these are exploited to cause harm to human life and property.

Wireless anomaly detection is a mature field with several unique solutions. This thesis aims to describe a novel way of detecting wireless anomalies using metadata analysis based methods. The metadata is processed and analyzed by a LSTM based Autoencoder and a LSTM based feature analyzer to produce a wide range of anomaly scores. The anomaly scores are then uploaded and analyzed to identify any anomalous fluctuations. An associated tool can also automatically download live data, train, test, and upload results to the Elasticsearch database. The overall method described is in sharp contrast to the more weathered solution of analyzing raw data from a Software Designed Radio, and has the potential to be scaled much more efficiently. Master of Science Wireless communications are a major part of our world. Detecting unusual changes in the wireless spectrum is therefore a high priority in maintaining networks and more. This paper describes a method that allows centralized processing of wireless network output, allowing monitoring of several areas simultaneously. This is in sharp contrast to other methods which generally must be located near the area being monitored. In addition, this implementation has the capability to be scaled more efficiently as the hardware required to monitor is less costly than the hardware required to process wireless data.

Legacy cyber-physical systems (CPSs) were designed without considering cybersecurity as a primary design tenet especially when considering their evolving operating environment. There are many examples of legacy systems including automotive control, navigation, transportation, and industrial control systems (ICSs), to name a few. To make matters worse, the cost of designing and deploying defenses in existing legacy infrastructure can be overwhelming as millions or even billions of legacy CPS systems are already in use. This economic angle, prevents the use of defenses that are not backward compatible. Moreover, any protection has to operate efficiently in resource constraint environments that are dynamic nature. Hence, the existing approaches that require ex- pensive additional hardware, propose a new protocol from scratch, or rely on complex numerical operations such as strong cryptographic solutions, are less likely to be deployed in practice. In this dissertation, we explore a variety of lightweight solutions for securing different existing CPSs without requiring any modifications to the original system design at hardware or protocol level. In particular, we use fingerprinting, crowdsourcing and deterministic models as alternative backwards- compatible defenses for securing vehicles, global positioning system (GPS) receivers, and a class of ICSs called supervisory control and data acquisition (SCADA) systems, respectively. We use fingerprinting to address the deficiencies in automobile cyber-security from the angle of controller area network (CAN) security. CAN protocol is the de-facto bus standard commonly used in the automotive industry for connecting electronic control units (ECUs) within a vehicle. The broadcast nature of this protocol, along with the lack of authentication or integrity guarantees, create a foothold for adversaries to perform arbitrary data injection or modification and impersonation attacks on the ECUs. We propose SIMPLE, a single-frame based physical layer identification for intrusion detection and prevention on such networks. Physical layer identification or fingerprinting is a method that takes advantage of the manufacturing inconsistencies in the hardware components that generate the analog signal for the CPS of our interest. It translates the manifestation of these inconsistencies, which appear in the analog signals, into unique features called fingerprints which can be used later on for authentication purposes. Our solution is resilient to ambient temperature, supply voltage value variations, or aging. Next, we use fingerprinting and crowdsourcing at two separate protection approaches leveraging two different perspectives for securing GPS receivers against spoofing attacks. GPS, is the most predominant non-authenticated navigation system. The security issues inherent into civilian GPS are exacerbated by the fact that its design and implementation are public knowledge. To address this problem, first we introduce Spotr, a GPS spoofing detection via device fingerprinting, that is able to determine the authenticity of signals based on their physical-layer similarity to the signals that are known to have originated from GPS satellites. More specifically, we are able to detect spoofing activities and track genuine signals over different times and locations and propagation effects related to environmental conditions. In a different approach at a higher level, we put forth Crowdsourcing GPS, a total solution for GPS spoofing detection, recovery and attacker localization. Crowdsourcing is a method where multiple entities share their observations of the environment and get together as a whole to make a more accurate or reliable decision on the status of the system. Crowdsourcing has the advantage of deployment with the less complexity and distributed cost, however its functionality is dependent on the adoption rate by the users. Here, we have two methods for implementing Crowdsourcing GPS. In the first method, the users in the crowd are aware of their approximate distance from other users using Bluetooth. They cross validate this approximate distance with the GPS-derived distance and in case of any discrepancy they report ongoing spoofing activities. This method is a strong candidate when the users in the crowd have a sparse distribution. It is also very effective when tackling multiple coordinated adversaries. For method II, we exploit the angular dispersion of the users with respect to the direction that the adversarial signal is being transmitted from. As a result, the users that are not facing the attacker will be safe. The reason for this is that human body mostly comprises of water and absorbs the weak adversarial GPS signal. The safe users will help the spoofed users find out that there is an ongoing attack and recover from it. Additionally, the angular information is used for localizing the adversary. This method is slightly more complex, and shows the best performance in dense areas. It is also designed based on the assumption that the spoofing attack is only terrestrial. Finally, we propose a tandem IDS to secure SCADA systems. SCADA systems play a critical role in most safety-critical infrastructures of ICSs. The evolution of communications technology has rendered modern SCADA systems and their connecting actuators and sensors vulnerable to malicious attacks on both physical and application layers. The conventional IDS that are built for securing SCADA systems are focused on a single layer of the system. With the tandem IDS we break this habit and propose a strong multi-layer solution which is able to expose a wide range of attack. To be more specific, the tandem IDS comprises of two parts, a traditional network IDS and a shadow replica. We design the shadow replica as a deterministic IDS. It performs a workflow analysis and makes sure the logical flow of the events in the SCADA controller and its connected devices maintain their expected states. Any deviation would be a malicious activity or a reliability issue. To model the application level events, we leverage finite state machines (FSMs) to compute the anticipated states of all of the devices. This is feasible because in many of the existing ICSs the flow of traffic and the resulting states and actions in the connected devices have a deterministic nature. Consequently, it leads to a reliable and free of uncertainty solution. Aside from detecting traditional network attacks, our approach bypasses the attacker in case it succeeds in taking over the devices and also maintains continuous service if the SCADA controller gets compromised. Doctor of Philosophy Our lives are entangled with cyber-physical systems (CPSs) on a daily basis. Examples of these systems are vehicles, navigation systems, transportation systems, industrial control systems, etc. CPSs are mostly legacy systems and were built with a focus on performance, overlooking security. Security was not considered in the design of these old systems and now they are dominantly used in our everyday life. After numerous demonstration of cyber hacks, the necessity of protecting the CPSs from adversarial activities is no longer ambiguous. Many of the advanced cryptographic techniques are far too complex to be implemented in the existing CPSs such as cars, satellites, etc. We attempt to secure such resource constraint systems using simple backward compatible techniques in this dissertation. We design cheap lightweight solutions, with no modifications to the original system. In part of our research, we use fingerprinting as a technique to secure passenger cars from being hacked, and GPS receivers from being spoofed. For a brief description of fingerprinting, we use the example of two identical T-shirts with the same size and design. They will always have subtle differences between them no matter how hard the tailor tried to make them identical. This means that there are no two T-shirts that are exactly identical. This idea, when applied to analog signalling on electric devices, is called fingerprinting. Here, we fingerprint the mini computers inside a car, which enables us to identify these computers and prevent hacking. We also use the signal levels to design fingerprints for GPS signals. We use the fingerprints to distinguish counterfeit GPS signals from the ones that have originated from genuine satellites. This summarizes two major contributions in the dissertation. Our earlier contribution to GPS security was effective, but it was heavily dependent on the underlying hardware, requiring extensive training for each radio receiver that it was protecting. To remove this dependence of training for the specific underlying hardware, we design and implement the next framework using defenses that require application-layer access. Thus, we proposed two methods that leverage crowdsourcing approaches to defend against GPS spoofing attacks and, at the same time, improve the accuracy of localization for commodity mobile devices. Crowdsourcing is a method were several devices agree to share their information with each other. In this work, GPS users share their location and direction information, and in case of any discrepancy they figure that they are under attack and cooperate to recover from it. Last, we shift the gear to the industrial control systems (ICSs) and propose a novel IDS to protect them against various cyber attacks. Unlike the conventional IDSs that are focused on one of the layers of the system, our IDS comprises of two main components. A conventional component that exposes traditional attacks and a second component called a shadow replica. The replica mimics the behavior of the system and compares it with that of the actual system in a real-time manner. In case of any deviation between the two, it detects attacks that target the logical flow of the events in the system. Note that such attacks are more sophisticated and difficult to detect because they do not leave any obvious footprints behind. Upon detection of attacks on the original controller, our replica takes over the responsibilities of the original ICS controller and provides service continuity.

Real-time computing systems are those that are designed to achieve computing goals by certain deadlines. Real-time computing systems are present in everything from cars to airplanes, pacemakers to industrial-control systems, and other pieces of critical infrastructure. With the increasing interconnectivity of these systems, system security issues and the constant threat of manipulation by malicious external attackers that have plagued general computing systems, now threaten the integrity and safety of real-time systems. This dissertation discusses three different defense techniques that focuses on the role that real-time scheduling theory can play to reduce runtime cost, and guarantee correctness when applying these defense strategies to real-time systems. The first work introduces a novel timing aware defense strategy for the CAN bus that utilizes TrustZone on state-of-the-art ARMv8-M microcontrollers. The second reduces the runtime cost of control-flow integrity (CFI), a popular system security defense technique, by correctly modeling when a real-time system performs I/O, and exploiting the model to schedule CFI procedures efficiently. Finally, the third studies and provides a lightweight mitigation strategy for a recently discovered vulnerability within mixed criticality real-time systems. Doctor of Philosophy Real-time computing systems are those that are designed to achieve computing goals within certain timing constraints. Real-time computing systems are present in everything from cars to airplanes, pacemakers to industrial-control systems, and other pieces of critical infrastructure. With the increasing interconnectivity of these systems, system security issues and the constant threat of manipulation by malicious external attackers that have plagued general computing systems, now threaten the integrity and safety of real-time systems. This dissertation discusses three different defense techniques that focuses on the role that real-time scheduling theory can play to reduce runtime cost, and guarantee correctness when applying these defense strategies to real-time systems. The first work introduces a novel timing aware defense strategy for the Controller Area Network (CAN). CAN is a popular communication system that is at the heart of every modern passenger vehicle and is indispensable for the safe operation of various components such as the engine and transmission systems, and due to its simplicity, may be vulnerable to a variety of attacks. We leverage security advancements in modern processor design to provide a lightweight and predictable (in terms of time taken to perform the operation) defense technique for some of these vulnerabilities. The second work applies a technique called Control-Flow Integrity (CFI) to real-time systems. CFI is a general-purpose defense technique to prevent attackers from modifying software execution, and applying such techniques to real-time systems, particularly those with limited hardware capabilities, may be infeasible. By applying real-time scheduling theory, we propose a strategy to apply CFI to such systems, while reducing its overhead, or cost, without compromising the security guarantees CFI inherently provides. Finally, safety-critical systems may consist of a mix of operations, each having a different level of importance (criticality) with respect to the safe operation of the system. However, due to the complexity of modeling such systems, the models themselves may be vulnerable to attacks. Through simulations we study one such vulnerability and propose a modification to mitigate it.

While increasing number of vehicles on urban roadways create uncontrolled congestion, connectivity among vehicles, traffic lights and other road-side units provide abundant data that paves avenues for novel smart traffic control mechanisms to mitigate traffic congestion and delays. However, increasingly complex vehicular applications have outpaced the computational capabilities of on-board processing units, therefore requiring novel offloading schemes onto additional resources located by the road-side. Adding connectivity and other computational resources on legacy traffic infrastructure may also introduce security vulnerabilities. To ensure that the timeliness and resource constraints of the vehicles using the roadways as well as the applications being deployed on the traffic infrastructure are met, the transportation systems needs to be more predictable. This dissertation discusses three areas that focus on improving the predictability and performance of the connected traffic infrastructure. Firstly, a holistic traffic control strategy is presented that ensures predictable traffic flow by minimizing traffic delays, accounting for unexpected traffic conditions and ensuring timely emergency vehicle traversal through an urban road network. Secondly, a vehicular edge resource management strategy is discussed that incorporates connected traffic lights data to meet timeliness requirements of the vehicular applications. Finally, security vulnerabilities in existing traffic controllers are studied and countermeasures are provided to ensure predictable traffic flow while thwarting attacks on the traffic infrastructure. Doctor of Philosophy Exponentially increasing vehicles especially in urban areas create pollution, delays and uncontrolled traffic congestion. However, improved traffic infrastructure brings connectivity among the vehicles, traffic lights, road-side detectors and other equipment, which can be leveraged to design new and advanced traffic control techniques. The initial work in this dissertation provides a traffic control technique that (i) reduces traffic wait times for the vehicles in urban areas, (ii) ensures safe and quick movements of emergency vehicles even through crowded areas, and (iii) ensures that the traffic keeps moving even under unexpected lane closures or roadblocks. As technology advances, connected vehicles are becoming increasingly automated. This allows the car manufacturers to design novel in-vehicle features where the passengers can now stream media-rich content, play augmented reality (AR)-based games and/or get high definition information about the surroundings on their car's display, while the car is driven through the urban traffic. This is made possible by providing additional computing resources along the road-side that the vehicles can utilize wirelessly to ensure passenger's comfort and improved experience of in-vehicle features. In this dissertation, a technique is provided to manage the computational resources which will allow vehicles (and its passengers) to use multiple features simultaneously. As the traffic infrastructure becomes increasingly inter-connected, it also allows malicious actors to exploit vulnerabilities such as modifying traffic lights, interfering with road-side sensors, etc. This can lead to increased traffic wait times and eventually bring down the traffic network. In the final work, one such vulnerability in traffic infrastructure is studied and mitigating measures are provided so that the traffic keeps moving even when an attack is detected. In all, this dissertation aims to improve safety, security and overall experience of the drivers, passengers and the pedestrians using the connected traffic infrastructure.

Critical Infrastructures (CIs), such as power, transportation, healthcare, etc., refer to systems, facilities, technologies, and networks vital to national security, public health, and socio-economic well-being of people. CIs play a crucial role in emergency management. For example, the recent Hurricane Ida, Texas Winter storm, colonial cyber-attack that occurred during 2021 in the US, shows the CIs are highly inter-dependent with complex interactions. Hence power system failures and shutdown of natural gas pipelines, in turn, led to debilitating impacts on communication, waste systems, public health, etc. Consider power failures during a disaster, such as a hurricane. Subject Matter Experts (SMEs) such as emergency management authorities may be interested in several decision-making tasks. Can we identify disaster phases in terms of the severity of damage from analyzing changes in power failures? Can we tell the SMEs which power grids or regions are the most affected during each disaster phase and need immediate action to recover? Answering these questions can help SMEs to respond quickly and send resources for fast recovery from damage. Can we systematically provide how the failure of different power grids may impact the whole CIs due to inter-dependencies? This can help SMEs to better prepare and mitigate the risks by improving system resiliency. In this thesis, we explore problems to efficiently operate decision-making tasks during a disaster for emergency management authorities. Our research has two primary directions, guide decision-making in resource allocation and plans to improve system resiliency. Our work is done in collaboration with the Oak Ridge National Laboratory to contribute impactful research in real-life CIs and disaster power failure data. 1. Explainable resource allocation: In contrast to the current interpretable or explainable model that provides answers to understand a model output, we view explanations as answers to guide resource allocation decision-making. In this thesis, we focus on developing a novel model and algorithm to identify disaster phases from changes in power failures. Also, pinpoint the regions which can get most affected at each disaster phase so the SMEs can send resources for fast recovery. 2. Networks for improving system resiliency: We view CIs as a large heterogeneous network with nodes as infrastructure components and dependencies as edges. Our goal is to construct a visual analytic tool and develop a domain-inspired model to identify the important components and connections to which the SMEs need to focus and better prepare to mitigate the risk of a disaster. Doctor of Philosophy Critical Infrastructure Systems (CIs) entitle multiple infrastructures valuable for maintaining public life and national security, e.g., power, water, transportation. US Federal Emergency Management Agency (FEMA) aims to protect the nation and citizens by mitigating all hazards during natural or man-made disasters. For this, they aim to adopt different decision-making strategies efficiently. E.g., During an ongoing disaster, when to quickly send resources, which regions to send resources first, etc. Besides, they also need to plan how to prepare for a future disaster and which CIs need maintenance to improve system resiliency. We explore several data-mining problems which can guide FEMA towards developing efficient decision-making strategies. Our thesis emphasizes explainable and network-based models and algorithms to help decision-making operations for emergency management experts by leveraging critical infrastructures data.

The conventional computing techniques are based on the assumption of a near constant source of input power. While this assumption is reasonable for high-end devices such as servers and mobile phones, it does not always hold in embedded devices. An increasing number of Internet of Things (IoTs) is powered by intermittent power supplies which harvest energy from ambient resources, such as vibrations. While the energy harvesters provide energy autonomy, they introduce uncertainty in input power. Intermittent computing techniques were proposed as a coping mechanism to ensure forward progress even with frequent power loss. They utilize non-volatile memory to store a snapshot of the system state as a checkpoint. The conventional security mechanisms do not always hold in intermittent computing. This research takes a top-down approach to design secure intermittent systems. To that end, we identify security threats, design a secure intermittent system, optimize its performance, and evaluate our design using embedded benchmarks. First, we identify vulnerabilities that arise from checkpoints and demonstrates potential attacks that exploit the same. Then, we identify the minimum security requirements for protecting intermittent computing and propose a generic protocol to satisfy the same. We then propose different security levels to configure checkpoint security based on application needs. We realize configurable intermittent security to optimize our generic secure intermittent computing protocol to reduce the overhead of introducing security to intermittent computing. Finally, we study the role of application in intermittent computing and study the various factors that affect the forward progress of applications in secure intermittent systems. This research highlights that power loss is a threat vector even in embedded devices, establishes the foundation for security in intermittent computing. Doctor of Philosophy The embedded systems are present in every aspect of life. They are available in watches, mobile phones, tablets, servers, health aids, home security, and other everyday useful technology. To meet the demand for powering up a rising number of embedded devices, energy harvesters emerged as a solution to provide an autonomous solution to power on low-power devices. With energy autonomy, came energy scarcity that introduced intermittent computing, where embedded systems operate intermittently because of lack of constant input power. The intermittent systems store snapshots of their progress as checkpoints in non-volatile memory and restore the checkpoints to resume progress. On the whole, the intermittent system is an emerging area of research that is being deployed in critical locations such as bridge health monitoring. This research is focused on securing intermittent systems comprehensively. We perform a top-down analysis to identify threats, mitigate them, optimize the mitigation techniques, and evaluate the implementation to arrive at secure intermittent systems. We identify security vulnerabilities that arise from checkpoints to demonstrate the weakness in intermittent systems. To mitigate the identified vulnerabilities, we propose secure intermittent solutions to protect intermittent systems using a generic protocol. Based on the implementation of the generic protocol and its performance, we propose several optimizations based on the needs of the application to securing intermittent systems. And finally, we benchmark the security properties using two-way relation between security and application in intermittent systems. With this research, we create a foundation for designing secure intermittent systems.

A cyber-physical system (CPS) is a complex integration of hardware and software components to perform well-defined tasks. Up to this point, many software-based attacks targeting the network and computation layers have been reported by the researchers. However, the physical layer attacks that utilize natural phenomena (e.g., electromagnetic waves) to manipulate safety-critic signals such as analog sensor outputs, digital data, and actuation signals have recently taken the attention. The purpose of this dissertation is to detect the weaknesses of cyber-physical systems against low-power Intentional Electromagnetic Interference (IEMI) attacks and provide hardware-level countermeasures. Actuators are irreplaceable components of electronic systems that control the physically moving sections, e.g., servo motors that control robot arms. In Chapter 2, the potential effects of IEMI attacks on actuation control are presented. Pulse Width Modulation (PWM) signal, which is the industry–standard for actuation control, is observed to be vulnerable to IEMI with specific frequency and modulated–waveforms. Additionally, an advanced attacker with limited information about the victim can prevent the actuation, e.g., stop the rotation of a DC or servo motor. For some specific actuator models, the attacker can even take the control of the actuators and consequently the motion of the CPS, e.g., the flight trajectory of a UAV. The attacks are demonstrated on a fixed-wing unmanned aerial vehicle (UAV) during varying flight scenarios, and it is observed that the attacker can block or take control of the flight surfaces (e.g., aileron) which results in a crash of the UAV or a controllable change in its trajectory, respectively. Serial communication protocols such as UART or SPI are widely employed in electronic systems to establish communication between peripherals (e.g., sensors) and controllers. It is observed that an adversary with the reported three-phase attack mechanism can replace the original victim data with the 'desired' false data. In the detection phase, the attacker listens to the EM leakage of the victim system. In the signal processing phase, the exact timing of the victim data is determined from the victim EM leakage, and in the transmission phase, the radiated attack waveform replaces the original data with the 'desired' false data. The attack waveform is a narrowband signal at the victim baud rate, and in a proof–of–concept demonstration, the attacks are observed to be over 98% effective at inducing a desired bit sequence into pseudorandom UART frames. Countermeasures such as twisted cables are discussed and experimentally validated in high-IEMI scenarios. In Chapter 4, a state-of-art electrical vehicle (EV) charger is assessed in IEMI attack scenarios, and it is observed that an attacker can use low–cost RF components to inject false current or voltage sensor readings into the system. The manipulated sensor data results in a drastic increase in the current supplied to the EV which can easily result in physical damage due to thermal runaway of the batteries. The current switches, which control the output current of the EV charger, can be controlled (i.e., turned on) by relatively high–power IEMI, which gives the attacker direct control of the current supplied to the EV. The attacks on UAVs, communication systems, and EV chargers show that additional hardware countermeasures should be added to the state-of-art system design to alleviate the effect of IEMI attacks. The fiber-optic transmission and low-frequency magnetic field shielding can be used to transmit 'significant signals' or PCB-level countermeasures can be utilized which are reported in Chapter 5. Doctor of Philosophy The secure operation of an electronic system depends on the integrity of the signals transmitted from/to components like sensors, actuators, and controllers. Adversaries frequently aim to block or manipulate the information carried in sensor and actuation signals to disrupt the operation of the victim system with physical phenomena, e.g., infrared light or acoustic waves. In this dissertation, it is shown that low-power electromagnetic (EM) waves, with specific frequency and form devised for the victim system, can be utilized as an attack tool to disrupt, and, in some scenarios, control the operation of the system; moreover, it is shown that these attacks can be mitigated with hardware-level countermeasures. In Chapter 2, the attacks are applied to electric motors on an unmanned aerial vehicle (UAV), and it is observed that an attacker can block (i.e., crash of the UAV) or control the UAV motion with EM waves. In Chapter 3, it is shown that digital communication systems are not resilient against intentional electromagnetic interference (IEMI), either. Low–power EM waves can be utilized by attackers to replace the data in serial communication systems with a success rate %98 or more. In Chapter 4, the attacks are applied to the sensors and actuators of electric vehicle chargers with low–cost over–the–shelf amplifiers and antennas, and it is shown that EM interference attacks can manipulate the sensor data and boosts the current supplied to the EV, which can result in overheating and fire. To ensure secure electronic system operation, hardware–level defense mechanisms are discussed and validated with analytical solutions, simulations, and experiments.

Traffic Collision Avoidance Systems (TCAS) are safety-critical, unauthenticated, ranging systems required in commercial aircraft. Previous work has proposed TCAS vulnerabilities to attacks from malicious actors with low cost software defined radios (SDR) and inexpensive open-source software (GNU radio) where spoofing TCAS radio signals in now possible. This paper outlines a proposed threat model for several TCAS vulnerabilities from an adversarial perspective. Periodic and aperiodic attack models are explored as possible low latency solutions to spoof TCAS range estimation. A TCAS test bed is established with commercial avionics to demonstrate the efficacy of proposed vulnerabilities. SDRs and Vector Waveform Generators (VWGs) are used to achieve desired latency. Sensor inputs to the TCAS system are spoofed with micro-controllers. These include Radar Altimeter, Barometric Altimeter, and Air Data Computer (ADC) heading and attitude information transmitted by Aeronautical Radio INC (ARINC) 429 encoding protocol. TCAS spoofing is attempted against the test bed and analysis conducted on the timing results and test bed performance indicators. The threat model is analyzed qualitatively and quantitatively. Master of Science Traffic Collision Avoidance Systems (TCAS), or Airborne Collision Avoidance Systems ACAS), are safety-critical systems required by the Federal Aviation Administration (FAA) in commercial aircraft. They work by sending queries to surrounding aircraft in the form of radio transmission. Aircraft in the who receive these transmissions send replies. Information in these replies allow the TCAS system to determine if a nearby aircraft may travel too close to itself. TCAS can then determine help both pilots avoid a mid-air collision. Information in the messages can be faked by a malicious actor. To explore these vulnerabilities a test bed is built with commercial grade TCAS equipment. Several types of attacks are evaluated.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 traditional internal combustion engine Chevrolet Blazer and to transform the vehicle into a P4 hybrid. Due to the P4 Hybrid architecture, the HEVT vehicle has an internal combustion engine on the front axle and an electric motor on the rear axle. The goal of this competition is to create a vehicle that achieves better fuel economy and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced vehicle technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of a full range longitudinal Adaptive Cruise Control (ACC) algorithm. Initially, research is conducted on various linear and nonlinear control strategies that provide the necessary functionality. Based on the ability to predict future time instances in an optimal method, the Model Predictive Control (MPC) algorithm is chosen and combined with other standard control strategies to create an ACC system. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy savings to the rider while maintaining safety as the priority. Rider comfort is achieved by placing constraints on acceleration and jerk. Lastly, a proper energy analysis is conducted to showcase the potential energy savings with the implementation of the Adaptive Cruise Control system. This implementation includes tuning the algorithm so that the best energy consumption at the wheel is achieved without compromising vehicle safety. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified nonlinear vehicle system model in MATLAB to simulate different conditions. For each condition, comfort and energy consumption are analyzed. The city 505 simulation of a traditional ACC system show a 14% or 42 Wh/mi reduction in energy at the wheel. The city 505 simulation of the environmentally friendly ACC system show a 29% or 88 Wh/mi reduction in energy at the wheel. Furthermore, these simulations confirm that maximum acceleration and jerk are bounded. Specifically, peak jerk is reduced by 90% or 8 m/s3 during a jerky US06 drive cycle. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces tractive energy consumption while improving rider comfort for any vehicle. Master of Science The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 Chevrolet Blazer into a hybrid. This modification is accomplished by creating a vehicle that burns less gasoline and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of Adaptive Cruise Control (ACC). Initially, research is conducted on various control strategies that provide the necessary functionality. A controller that predicts future events is selected for the Adaptive Cruise Control. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy consumption savings to the rider while maintaining safety as the priority. Rider comfort is achieved by creating a smoother ride. Lastly, a proper energy analysis showcases the potential energy savings with the implementation of the Adaptive Cruise Control system. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified vehicle model to simulate different conditions. The city simulations of a traditional ACC system show a 14% reduction in energy at the wheel. City simulations of the environmentally friendly Adaptive Cruise Controller show a 29% reduction in energy. Both of these simulations allow for comfortable ride. Specifically, maximum car jerk is reduced by 90%. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces energy consumption at the wheel while improving rider comfort.

Recent breakthroughs in microscopy techniques and fluorescence probes enable the recording of mouse embryogenesis at the cellular level for days, easily generating terabyte-level 3D time-lapse data. Since millions of cells are involved, this information-rich data brings a natural demand for an automated tool for its comprehensive analysis. This tool should automatically (1) detect and segment cells at each time point and (2) track cell migration across time. Most existing cell tracking methods cannot scale to the data with such large size and high complexity. For those purposely designed for embryo data analysis, the accuracy is heavily sacrificed. Here, we present a new computational framework for the mouse embryo data analysis with high accuracy and efficiency. Our framework detects and segments cells with a fully probability-principled method, which not only has high statistical power but also helps determine the desired cell territories and increase the segmentation accuracy. With the cells detected at each time point, our framework reconstructs cell traces with a new minimum-cost circulation-based paradigm, CINDA (CIrculation Network-based DataAssociation). Compared with the widely used minimum-cost flow-based methods, CINDA guarantees the global optimal solution with the best-of-known theoretical worst-case complexity and hundreds to thousands of times practical efficiency improvement. Since the information extracted from a single time point is limited, our framework iteratively refines cell detection and segmentation results based on the cell traces which contain more information from other time points. Results show that this dramatically improves the accuracy of cell detection, segmentation, and tracking. To make our work easy to use, we designed a standalone software, MIVAQ (Microscopic Image Visualization, Annotation, and Quantification), with our framework as the backbone and a user-friendly interface. With MIVAQ, users can easily analyze their data and visually check the results. Doctor of Philosophy Mouse embryogenesis studies mouse embryos from fertilization to tissue and organ formation. The current microscope and fluorescent labeling technique enable the recording of the whole mouse embryo for a long time with high resolution. The generated data can be terabyte-level and contains more than one million cells. This information-rich data brings a natural demand for an automated tool for its comprehensive analysis. This tool should automatically (1) detect and segment cells at each time point to get the information of cell morphology and (2) track cell migration across time. However, the development of analytical tools lags far behind the capability of data generation. Existing tools either cannot scale to the data with such large size and high complexity or sacrifice accuracy heavily for efficiency. In this dissertation, we present a new computational framework for the mouse embryo data analysis with high accuracy and efficiency. To make our framework easy to use, we also designed a standalone software, MIVAQ, with a user-friendly interface. With MIVAQ, users can easily analyze their data and visually check the results.

Internet-of-Things (IoT) devices have become increasingly popular in the last 10 years, yet also show an acceptance for lack of security due to hardware constraints. The range of sophistication in IoT devices varies substantially depending on the functionality required, so security options need to be flexible. Manufacturers typically either use no security, or lean towards the use of the Advanced Encryption Standard (AES) with a 128-bit key. AES-128 is suitable for the higher end of that IoT device range, but is costly enough in terms of memory, time, and energy consumption that some devices opt to use no security. Short development and a strong drive to market also contribute to a lack in security. Recent work in lightweight cryptography has analyzed the suitability of custom protocols using AES as a comparative baseline. AES outperforms most custom protocols when looking at security, but those analyses fail to take into account block size and future capabilities such as quantum computers. This thesis analyzes lightweight cryptographic primitives that would be suitable for use in IoT devices, helping fill a gap for "good enough" security within the size, weight, and power (SWaP) constraints common to IoT devices. The primitives have not undergone comprehensive cryptanalysis and this thesis attempts to provide a preliminary analysis of confidentiality. The first is a single-stage residue number system (RNS) pseudorandom number generator (PRNG) that was shown in previous publications to produce strong outputs when analyzed with statistical tests like the NIST RNG test suite and DIEHARD. However, through analysis, an intelligent multi-stage conditional probability attack based on the pigeonhole principle was devised to reverse engineer the initial state (key) of a single-stage RNS PRNG. The reverse engineering algorithm is presented and used against an IoT-caliber device to showcase the ability of an attacker to retrieve the initial state. Following, defenses based on intentional noise, time hopping, and code hopping are proposed. Further computation and memory analysis show the proposed defenses are simple in implementation, but increase complexity for an attacker to the point where reverse engineering the PRNG is likely no longer viable. The next primitive proposed is a block cipher combination technique based on Galois Extension Field multiplication. Using any PRNG to produce the pseudorandom stream, the block cipher combination technique generates a variable sized key matrix to encrypt plaintext. Electronic Codebook (ECB) and Cipher Feedback (CFB) modes of operation are discussed. Both system modes are implemented in MATLAB as well as on a Texas Instruments (TI) MSP430FR5994 microcontroller for hardware validation. A series of statistical tests are then run against the simulation results to analyze overall randomness, including NIST and the Law of the Iterated Logarithm; the system passes both. The implementation on hardware is compared against a stream cipher variation and AES-128. The block cipher proposed outperforms AES-128 in terms of computation time and consumption for small block sizes. While not as secure, the cryptosystem is more scalable to block sizes used in IoT devices. Master of Science An Internet-of-Things (IoT) device is a single-purpose computer that operates with less computing resources and sometimes on battery power. The classification of IoT can range anywhere from motion sensors to a doorbell camera, but IoT devices are used in more than just home automation. The medical and industrial spaces use simple wireless computers for a number of tasks as well. One concern with IoT, given the hardware constraints, is the lack of security. Since messages are often transmitted through a wireless medium, anybody could eavesdrop on what is being communicated if data is not encrypted prior to transmission. Cryptography is the practice of taking any string of data and obfuscating it through a process that only valid parties can reverse. The sophistication of cryptographic systems has increased to the point where IoT manufacturers elect to use no security in many cases because the hardware is not advanced enough to run them efficiently. The Advanced Encryption Standard (AES) is usually the choice for security in the IoT space, but typically only higherend devices can afford to use AES. This thesis focuses on alternative lightweight systems to AES. First, a single-stage residue number system (RNS) pseudorandom number generator (PRNG) is analyzed, which has been proven to generate statistically random outputs in previous publications. PRNGs are a cheap method of producing seemingly random outputs through an algorithm once provided with an initial state known as a seed. An intelligent attack on the PRNG is devised, which is able to reverse engineer the initial state, effectively breaking the random behavior. Three defenses against the attack are then implemented to protect against the reported vulnerability. Following, a block cipher combination technique is presented, using the aforementioned PRNG as the source of randomness. A block cipher is a method of encrypting large chunks of data together, to better obfuscate the output. Using a block cipher is more secure than just using a PRNG for encryption. However, PRNGs are used to generate the key for the proposed block cipher, as they offer a more efficient method of security. The combination technique presented serves to increase the security of PRNGs further. The cipher is shown to perform better on an IoT-caliber device in terms of computation time and energy consumption at smaller block sizes than AES.

Controlling an exoskeleton glove with a highly efficient human-machine interface (HMI), while accurately applying force to each joint remains a hot topic. This paper proposes a fast, secure, accurate, and portable solution to control an exoskeleton glove. This state of the art solution includes both hardware and software components. The exoskeleton glove uses a modified serial elastic actuator (SEA) to achieve accurate force sensing. A portable electronic system is designed based on the SEA to allow force measurement, force application, slip detection, cloud computing, and a power supply to provide over 2 hours of continuous usage. A voice-control-based HMI referred to as the integrated trigger-word configurable voice activation and speaker verification system (CVASV), is integrated into a robotic exoskeleton glove to perform high-level control. The CVASV HMI is designed for embedded systems with limited computing power to perform voice-activation and voice-verification simultaneously. The system uses MobileNet as the feature extractor to reduce computational cost. The HMI is tuned to allow better performance in grasping daily objects. This study focuses on applying the CVASV HMI to the exoskeleton glove to perform a stable grasp with force-control and slip-detection using SEA based exoskeleton glove. This research found that using MobileNet as the speaker verification neural network can increase the speed of processing while maintaining similar verification accuracy. Master of Science The robotic exoskeleton glove used in this research is designed to help patients with hand disabilities. This thesis proposes a voice-activated grasping system to control the exoskeleton glove. Here, the user can use a self-defined keyword to activate the exoskeleton and use voice to control the exoskeleton. The voice command system can distinguish between different users' voices, thereby improving the safety of the glove control. A smartphone is used to process the voice commands and send them to an onboard computer on the exoskeleton glove. The exoskeleton glove then accurately applies force to each fingertip using a force feedback actuator.This study focused on designing a state of the art human machine interface to control an exoskeleton glove and perform an accurate and stable grasp.

Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. Generating music is very difficult; components like long and short term structure present time complexity, which can be difficult for neural networks to capture. Additionally, the acoustics of musical features like harmonies and chords, as well as timbre and instrumentation require complex representations for a network to accurately generate them. Various techniques for both music representation and network architecture have been used in the past decade to address these challenges in music generation. The focus of this thesis extends beyond generating music to the challenge of controlling and/or conditioning that generation. Conditional generation involves an additional piece or pieces of information which are input to the generator and constrain aspects of the results. Conditioning can be used to specify a tempo for the generated song, increase the density of notes, or even change the genre. Latent walking is one of the most popular techniques in conditional image generation, but its effectiveness on music-domain generation is largely unexplored. This paper focuses on latent walking techniques for conditioning the music generation network MuseGAN and examines the impact of this conditioning on the generated music. Master of Science Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. Beyond simply generating music lies the challenge of controlling or conditioning that generation. Conditional generation can be used to specify a tempo for the generated song, increase the density of notes, or even change the genre. Latent walking is one of the most popular techniques in conditional image generation, but its effectiveness on music-domain generation is largely unexplored, especially for generative adversarial networks (GANs). This paper focuses on latent walking techniques for conditioning the music generation network MuseGAN and examines the impact and effectiveness of this conditioning on the generated music.

Radio frequency fingerprinting, a type of physical layer identification, allows identifying wireless transmitters based on their unique hardware. Every wireless transmitter has slight manufacturing variations and differences due to the layout of components. These are manifested as differences in the signal emitted by the device. A variety of techniques have been proposed for identifying transmitters, at the physical layer, based on these differences. This has been successfully demonstrated on a large variety of transmitters and other devices. However, some situations still pose challenges: Some types of fingerprinting feature are very dependent on the modulated signal, especially features based on the frequency content of a signal. This means that changes in transmitter configuration such as bandwidth or modulation will prevent wireless fingerprinting. Such changes may occur frequently with cognitive radios, and in dynamic spectrum access networks. A method is proposed to transform features to be invariant with respect to changes in transmitter configuration. With the transformed features it is possible to re-identify devices with a high degree of certainty. Next, improving performance with limited data by identifying devices using observations crowdsourced from multiple receivers is examined. Combinations of three types of observations are defined. These are combinations of fingerprinter output, features extracted from multiple signals, and raw observations of multiple signals. Performance is demonstrated, although the best method is dependent on the feature set. Other considerations are considered, including processing power and the amount of data needed. Finally, drift in fingerprinting features caused by changes in temperature is examined. Drift results from gradual changes in the physical layer behavior of transmitters, and can have a substantial negative impact on fingerprinting. Even small changes in temperature are found to cause drift, with the oscillator as the primary source of this drift (and other variation) in the fingerprints used. Various methods are tested to compensate for these changes. It is shown that frequency based features not dependent on the carrier are unaffected by drift, but are not able to distinguish between devices. Several models are examined which can improve performance when drift is present. Doctor of Philosophy Radio frequency fingerprinting allows uniquely identifying a transmitter based on characteristics of the signal it emits. In this dissertation several extensions to current fingerprinting techniques are given. Together, these allow identification of transmitters which have changed the signal sent, identifying using different measurement types, and compensating for variation in a transmitter's behavior due to changes in temperature.

This dissertation explores several challenges that plague the hardware-software co-design of popular resource constrained real-time embedded systems. We specifically tackle existing real-world problems, and address them through our design solutions which are highly scalable, and have practical feasibility as verified through our solution implementation on real-world hardware. We address the problem of poor battery life in mobile embedded devices caused due to side-by-side execution of multiple applications in split-screen mode. Existing industry solutions either restricts the number of applications that can run simultaneously, limit their functionality, and/or increase the hardware capacity of the battery associated with the system. We exploit the gap in research on performance and power trade-off in smartphones to propose an integrated energy management solution, that judiciously minimizes the system-wide energy consumption with negligible effect on its quality of service (QoS). Another important real-world requirement in today's interconnected world is the need for security. In the domain of real-time computing, it is not only necessary to secure the system but also maintain its timeliness. Some example security mechanisms that may be used in a hard real-time system include, but are not limited to, security keys, protection of intellectual property (IP) of firmware and application software, one time password (OTP) for software certification on-the-fly, and authenticated computational off-loading. Existing design solutions require expensive, custom-built hardware with long time-to-market or time-to-deployment cycle. A readily available alternative is the use of trusted execution environment (TEE) on commercial off-the-shelf (COTS) embedded processors. However, utilizing TEE creates multiple challenges from a real-time perspective, which includes additional time overhead resulting in possible deadline misses. Second, trusted execution may adversely affect the deterministic execution of the system, as tasks running inside a TEE may need to communicate with other tasks that are executing on the native real-time operating system. We propose three different solutions to address the need for a new task model that can capture the complex relationship between performance and predictability for real-time tasks that require secure execution inside TEE. We also present novel task assignment and scheduling frameworks for real-time trusted execution on COTS processors to improve task set schedulability. We extensively assess the pros and cons of our proposed approaches in comparison to the state-of-the-art techniques in custom-built real-world hardware for feasibility, and simulated environments to test our solutions' scalability. Doctor of Philosophy Today's real-world problems demand real-time solutions. These solutions need to be practically feasible, and scale well with increasing end user demands. They also need to maintain a balance between system performance and predictability, while achieving minimum energy consumption. A recent example of technological design problem involves ways to improve the battery lifetime of mobile embedded devices, for example, smartphones, while still achieving the required performance objectives. For instance, smartphones that run Android OS has the capability to run multiple applications concurrently using a newly introduced split-screen mode of execution, where applications can run side-by-side at the same time on screen while using the same shared resources (e.g., CPU, memory bandwidth, peripheral devices etc.). While this can improve the overall performance of the system, it can also lead to increased energy consumption, thereby directly affecting the battery life. Another technological design problem involves ways to protect confidential proprietary information from being siphoned out of devices by external attackers. Let us consider a surveillance unmanned aerial vehicle (UAV) as an example. The UAV must perform sensitive tasks, such as obtaining coordinates of interest for surveillance, within a given time duration, also known as task deadline. However, an attacker may learn how the UAV communicates with ground control, and take control of the UAV, along with the sensitive information it carries. Therefore, it is crucial to protect such sensitive information from access by an unauthorized party, while maintaining the system's task deadlines. In this dissertation, we explore these two real-world design problems in depth, observe the challenges associated with them, while presenting several solutions to tackle the issues. We extensively assess the pros and cons of our proposed approaches in comparison to the state-of- the-art techniques in custom-built real-world hardware, and simulated environments to test our solutions' scalability.

Modern enterprise-grade systems with virtually unlimited resources have many options when it comes to implementing state of the art intrusion prevention and detection solutions. These solutions are costly in terms of energy, execution time, circuit board area, and capital. Sustainable Internet of Things devices and power-constrained embedded systems are thus forced to make suboptimal security trade-offs. One such trade-off is the design of architectures which prevent execution of injected shell code, yet have allowed Return Oriented Programming (ROP) to emerge as a more reliable way to execute malicious code following attacks. ROP is a method used to take over the execution of a program by causing the return address of a function to be modified through an exploit vector, then returning to small segments of otherwise innocuous code located in executable memory one after the other to carry out the attacker's aims. We show that the Tiva TM4C123GH6PM microcontroller, which utilizes anARM Cortex-M4F processor, can be fully controlled with this technique. Firmware code is pre-loaded into a ROM on Tiva microcontrollers which can be subverted to erase and rewrite the flash memory where the program resides. That same firmware is searched for a Turing-complete gadget set which allows for arbitrary execution. We then design and evaluate a method for verifying the integrity of firmware on embedded systems, in this case Solid State Drives (SSDs). Some manufacturers make firmware updates available, but their proprietary protections leave end users unable to verify the authenticity of the firmware post installation. This means that attackers who are able to get a malicious firmware version installed on a victim SSD are able to operate with full impunity, as the owner will have no tools for detection. We have devised a method for performing side channel analysis of the current drawn by an SSD, which can compare its behavior while running genuine firmware against its behavior when running modified firmware. We train a binary classifier with samples of both versions and are able to consistently discriminate between genuine firmware and modified firmware, even despite changes in external factors such as temperature and supplied power. Doctor of Philosophy To most consumers and enterprises, a computer is the desktop or laptop device they use to run applications or write reports. Security for these computers has been a top priority since the advent of the Internet and the security landscape has matured considerably since that time. Yet, these consumer-facing computers are outnumbered several times over by embedded computers and microcontrollers which power ubiquitous systems in industrial control, home automation, and the Internet of Things. Unfortunately, the security landscape for these embedded systems is in relative infancy. Security controls designed for consumer and enterprise computers are often poorly suited for embedded system due to constraints such as power, memory, processing, and real-time performance demands. This research considers the unique constraints of embedded systems and analyzes their security in a practical way. We begin by exploring the mechanism and extent to which a device can be compromised. We show that a technique known as Return Oriented Programming (ROP) can be used to bypass some of the process control protections in place and that there can be enough existing code in the firmware to allow an attacker to execute code at will. This leads naturally to the question of how embedded computers can be secured. One important security assurance is the knowledge that a device is running legitimate firmware. This can be difficult for a device owner to verify due to proprietary protections put in place by manufacturers. However, we contribute a method to detect modifications to firmware on embedded systems, particularly Solid State Drives. This is done through an analysis of the current drawn during drive operations with best-practice data classification techniques. The findings of this research indicate that current embedded devices present a larger surface area for attack, less sophistication required for attack, and a larger quantity of devices vulnerable to attack. Even though these findings should raise concern, we also found that there are practical methods for detecting attack via monitoring and analysis.

Traffic congestion and safety has become a major issue in the modern world's commute. Congestion has been causing people to travel billions of hours more and to purchase billions of gallons of fuel extra which account to congestion cost of billions of dollars. Autonomous driving vehicles have been one solution to this problem because of their huge impact on efficiency, pollution, and human safety. Also, extensive research has been carried out on control design of vehicular platoons because a further improvement in traffic throughput while not compromising the safety is possible when the vehicles in the platoon are provided with better predictive abilities. Motion control is a key area of autonomous driving research that handles moving parts of vehicles in a deliberate and controlled manner. A widely worked on problem in motion control concerned with time parameterized reference tracking is trajectory tracking. Having an efficient and effective tracking algorithm embedded in the autonomous driving system is the key for better performance in terms of resources consumed and tracking error. Many tracking control algorithms in literature rely on an accurate model of the vehicle and often, it can be an intimidating task to come up with an accurate model taking into consideration various conditions like friction, heat effects, ageing processes etc. And typically, control algorithms rely on periodic execution of the tasks that update the control actions, but such updates might not be required, which result in unnecessary actions that waste resources. The main focus of this work is to design an intermittent model-free optimal control algorithm in order to enable autonomous vehicles to track trajectories at high-speeds. To obtain a solution which is model-free, a Q-learning setup with an actor-network to approximate the optimal intermittent controller and a critic network to approximate the optimal cost, resulting in the appropriate tuning laws is considered. Master of Science A risen research effort in the area of autonomous vehicles has been witnessed in the past few decades because these systems improve safety, comfort, transport time and energy consumption which are some of the main issues humans are facing in the modern world’s highway systems. Systems like emergency braking, automatic parking, blind angle vehicle detection are creating a safer driving environment in populated areas. Advanced driver assistance systems (ADAS) are what such kind of systems are known as. An extension of these partially automated ADAS are vehicles with fully automated driving abilities, which are able to drive by themselves without any human involvement. An extensively proposed approach for making traffic throughput more efficient on existing highways is to assemble autonomous vehicles into platoons. Small intervehicle spacing and many vehicles constituting each platoon formation improve the traffic throughput significantly. Lately, the advancements in computational capabilities, in terms of both algorithms and hardware, communications, and navigation and sensing devices contributed a lot to the development of autonomous systems (both single and multiagent) that operate with high reliability in uncertain/dynamic operating conditions and environments. Motion control is an important area in the autonomous vehicles research. Trajectory-tracking is a widely studied motion control scenario which is about designing control laws that force a system to follow some time-dependent reference path and it is important to have an effective and efficient trajectory-tracking control law in an autonomous vehicle to reduce the resources consumed and tracking error. The goal of this work is to design an intermittent model-free trajectory tracking control algorithm where there is no need of any mathematical model of the vehicle system being controlled and which can reduce the controller updates by allowing the system to evolve in an open loop fashion and close the loop only when an user defined triggering condition is satisfied. The approach is energy efficient in that the control updates are limited to instances when they are needed rather than unnecessary periodic updates. Q-learning which is a model-free reinforcement learning technique is used in the trajectory tracking motion control algorithm to make the vehicles track their respective reference trajectories without any requirement of their motion model, the knowledge of which is generally needed when dealing with a motion control problem. The testing of the designed algorithm in simulations and experiments is presented in this work. The study and development of a vehicle platform in order to perform the experiments is also discussed. Different motion control and sensing techniques are presented and used. The vehicle platform is shown to track a reference trajectory autonomously without any human intervention, both in simulations and experiments, proving the effectiveness of the proposed algorithm.

Deep learning has become an ubiquitous part of research in all fields, including wireless communications. Researchers have shown the ability to leverage deep neural networks (DNNs) that operate on raw in-phase and quadrature samples, termed Radio Frequency Machine Learning (RFML), to synthesize new waveforms, control radio resources, as well as detect and classify signals. While there are numerous advantages to RFML, this thesis answers the question "is it secure?" DNNs have been shown, in other applications such as Computer Vision (CV), to be vulnerable to what are known as adversarial evasion attacks, which consist of corrupting an underlying example with a small, intelligently crafted, perturbation that causes a DNN to misclassify the example. This thesis develops the first threat model that encompasses the unique adversarial goals and capabilities that are present in RFML. Attacks that occur with direct digital access to the RFML classifier are differentiated from physical attacks that must propagate over-the-air (OTA) and are thus subject to impairments due to the wireless channel or inaccuracies in the signal detection stage. This thesis first finds that RFML systems are vulnerable to current adversarial evasion attacks using the well known Fast Gradient Sign Method originally developed for CV applications. However, these current adversarial evasion attacks do not account for the underlying communications and therefore the adversarial advantage is limited because the signal quickly becomes unintelligible. In order to envision new threats, this thesis goes on to develop a new adversarial evasion attack that takes into account the underlying communications and wireless channel models in order to create adversarial evasion attacks with more intelligible underlying communications that generalize to OTA attacks. Master of Science Deep learning is beginning to permeate many commercial products and is being included in prototypes for next generation wireless communications devices. This technology can provide huge breakthroughs in autonomy; however, it is not sufficient to study the effectiveness of deep learning in an idealized laboratory environment, the real world is often harsh and/or adversarial. Therefore, it is important to know how, and when, these deep learning enabled devices will fail in the presence of bad actors before they are deployed in high risk environments, such as battlefields or connected autonomous vehicle communications. This thesis studies a small subset of the security vulnerabilities of deep learning enabled wireless communications devices by attempting to evade deep learning enabled signal classification by an eavesdropper while maintaining effective wireless communications with a cooperative receiver. The primary goal of this thesis is to define the threats to, and identify the current vulnerabilities of, deep learning enabled signal classification systems, because a system can only be secured once its vulnerabilities are known.

Security threats to cyber-physical systems are targeting institutions and infrastructure around the world, and the frequency and severity of attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or banks; it also includes critical infrastructure. Wireless Sensors Networks, Vehicle-to-everything communication (V2X), Dynamic Message Signs (DMS), and Traffic Signal Controllers are among major Intelligent Transportation Systems (ITS) infrastructure that has already been attacked or remain vulnerable to hacking. ITS has been deployed with a focus on increasing efficiency and safety in the face of dramatic increases in travel demand. Although many studies have been performed and many security primitives have been proposed, there are significant concerns about flawless performance in a dynamic environment. A holistic security approach, in which all infrastructure performs within the satisfactory level of security remains undiscovered. Previously, hacking of road infrastructure was a rare event, however, in recent years, field devices such as DMS are hacked with higher frequency. The primary reason that transportation assets are vulnerable to cyber-attacks is due to their location. A more dramatic scenario occurs when hackers attempt to convey tampered instructions to the public. Analyzing traveler behavior in response to the hacked messages sign on the basis of empirical data is a vital step toward operating a secure and reliable transportation system. There may be room for improvement by policymakers and program managers when considering critical infrastructure vulnerabilities. With cybersecurity issues escalating every day, road users' safety has been neglected. This dissertation overcomes these challenges and contributes to the nascent but growing literature of Intelligent Transportation System (ITS) security impact-oriented risk assessment in threefold. • First, I employ a risk-based approach to conduct a threat assessment. This threat assessment performs a qualitative vulnerability-oriented threat analysis. The objective is to scrutinize safety, security, reliability, and operation issues that are prompted by a compromised Dynamic Message Signs (DMS). • Second, I examine the impact of drivers' attitudes and behaviors on compliance, route diversion behavior, and speed change behavior, under a compromised DMS. We aim to assess the determinants that are likely to contribute to drivers' compliance with forged information. To this extent, this dissertation evaluates drivers' behavior under different unauthentic messages to assess in-depth the impact of an adversarial attack on the transportation network. • Third, I evaluate distracted driving under different scenarios to assess the in-depth impact of an adversarial attack on the transportation network. To this extent, this dissertation examines factors that are contributing to the manual, visual, and cognitive distractions when drivers encountering fabricated advisory information at a compromised DMS. The results of this dissertation support the original hypothesis and indicate that with respect to the forged information drivers tend to (1) change their planned route, (2) become involved in distracting activities, and (3) change their choice speed at the presence of a compromised DMS. The main findings of this dissertation are outlined below: 1. The DMS security vulnerabilities and predisposing conditions allow adversaries to compromise ITS functionality. The risk-based approach of this study delivers the impact-likelihood matrix, which maps the adverse impacts of the threat events onto a meaningful, visual, matrix. DMS hacking adverse impacts can be categorized mainly as high-risk and medium-risk clusters. The safety, operational (i.e., monetary losses) and behavioral impacts are associated with a high-risk cluster. While the security, reliability, efficiency, and operational (i.e., congestion) impacts are associated with the medium-risk cluster. 2. Tech friendly drivers are more likely to change their route under a compromised DMS. At the same time, while they are acquiring new information, they need to lowering their speed to respond to the higher information load. Under realistic-fabricated information, about 65% of the subjects would depart from their current route. The results indicate that females and subjects with a higher driving experience are more likely to change their route. In addition, those subjects who are more sensitive to the DMS's traffic-related messages and those who use DMS under congested traffic condition are more likely to divert. Interestingly, individuals with lower education level, Asians, those who live in urban areas, and those with trouble finding their direction in new routes are less likely to pick another route rather the one they planned for. 3. Regardless of the DMS hacking scenarios, drivers would engage in at least one of the distractive activities. Among the distractive activities, cognitive distraction has the highest impact on the distracted driving likelihood. Meaning, there is a high chance that drivers think of something other than driving, look at surrounding traffic and scenery, or talk to other passengers regarding the forged information they saw on the DMS. Drivers who rely and trust in technology, and those who check traffic condition before starting their trips tend to become distracted. In addition, the result identified that at the presence of bogus information, drivers tend to slow down or stop in order to react to the DMS. That is, they would either (1) become involved in activities through the means of their phone, (2) they would mind wander, look around, and talk to a passenger about the sign, and (3) search for extra information by means of their vehicle's radio or internet. 4. Females, black individuals, subjects with a disability, older, and those with high trust in DMS are less likely to ignore the fabricated messages. In contrary, white, those who drive long hours, and those who see driving as a tedious task are more likely to ignore the bogus messages. Drivers who comply with traffic regulations and have a good driving record are likely to slow down under the tampered messages. Furthermore, female drivers and those who live in rural areas are more likely to slow down under fabricated advisory information. Furthermore, this dissertation identifies that planning for alternative route and involvement in distractive activities cause speed variation behaviors under the compromised DMS. This dissertation is the first to investigate the adverse impact of a compromised DMS on the road users and operators. I attempt to address the current gap in the literature by assessing and evaluating the impact of ITS security vulnerabilities. Broader impacts of this study include (1) to systematically raising awareness among policy-makers and engineers, (2) motivating further simulations and real-world experiments to investigate this matter further, (3) to systematically assessing the adverse impact of a security breach on transportation reliability and safety, and drivers' behavior, and (4) providing insights for system operators and decision-makers to prioritize the risk of a compromised DMS. Additionally, the outcome can be integrated with the nationwide connected vehicle and V2X implementations and security design. Doctor of Philosophy Security threats are targeting institutions and infrastructure around the world, and the frequency and severity of security attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or banks; it also includes critical infrastructure. Intelligent Transportation Systems have been deployed with a focus on increasing efficiency and safety in the face of dramatic increases in traffic volume. Although many studies have been performed and many security primitives have been proposed, there are significant concerns about flawless performance in a dynamic environment. A holistic security approach, in which all infrastructure performs within the satisfactory level of security remains undiscovered. Previously, hacking of road infrastructure was a rare event, however, in recent years, field devices, such as dynamic message signs, are hacked with higher frequency. The primary reason that transportation assets are vulnerable to cyber-attacks is that of their location in public. A more dramatic scenario occurs when hackers attempt to convey tampered instructions to the public. Analyzing traveler behavior in response to the hacked messages sign on the basis of empirical data is a vital step toward operating a secure and reliable transportation system. This study is the first to investigate the adversarial impact of a compromised message sign on the road users and operators. I attempt to address the current gap in the literature by assessing and evaluating the impact of ITS security vulnerabilities.

Cyber-physical systems (CPSs) are systems that integrate physical elements with a cyber layer that enables sensing, monitoring, and processing the data from the physical components. Examples of CPSs include autonomous vehicles, unmanned aerial vehicles (UAVs), smart grids, and the Internet of Things (IoT). In particular, many critical infrastructure (CI) that are vital to our modern day cities and communities, are CPSs. This wide range of CPSs domains represents a cornerstone of smart cities in which various CPSs are connected to provide efficient services. However, this level of connectivity has brought forward new security challenges and has left CPSs vulnerable to many cyber-physical attacks and disruptive events that can utilize the cyber layer to cause damage to both cyber and physical components. Addressing these security and operation challenges requires developing new security solutions to prevent and mitigate the effects of cyber and physical attacks as well as improving the CPSs response in face of disruptive events, which is known as the CPS resilience. To this end, the primary goal of this dissertation is to develop novel analytical tools that can be used to study, analyze, and optimize the resilience and security of critical CPSs. In particular, this dissertation presents a number of key contributions that pertain to the security and the resilience of multiple CPSs that include power systems, the Internet of Things (IoT), UAVs, and transportation networks. First, a mathematical framework is proposed to analyze and mitigate the effects of GPS spoofing attacks against UAVs. The proposed framework uses system dynamics to model the optimal routes which UAVs can follow in normal operations and under GPS spoofing attacks. A countermeasure mechanism, built on the premise of cooperative localization, is then developed to mitigate the effects of these GPS spoofing attacks. To practically deploy the proposed defense mechanism, a dynamic Stackelberg game is formulated to model the interactions between a GPS spoofer and a drone operator. The equilibrium strategies of the game are analytically characterized and studied through a novel, computationally efficient algorithm. Simulation results show that, when combined with the Stackelberg strategies, the proposed defense mechanism will outperform baseline strategy selection techniques in terms of reducing the possibility of UAV capture. Next, a game-theoretic framework is developed to model a novel moving target defense (MTD) mechanism that enables CPSs to randomize their configurations to proactive deter impending attacks. By adopting an MTD approach, a CPS can enhance its security against potential attacks by increasing the uncertainty on the attacker. The equilibrium of the developed single-controller, stochastic MTD game is then analyzed. Simulation results show that the proposed framework can significantly improve the overall utility of the defender. Third, the concept of MTD is coupled with new cryptographic algorithms for enhancing the security of an mHealth Internet of Things (IoT) system. In particular, using a combination of theory and implementation, a framework is introduced to enable the IoT devices to update their cryptographic keys locally to eliminate the risk of being revealed while they are shared. Considering the resilience of CPSs, a novel framework for analyzing the component- and system-level resilience of CIs is proposed. This framework brings together new ideas from Bayesian networks and contract theory – a Nobel prize winning theory – to define a concrete system-level resilience index for CIs and to optimize the allocation of resources, such as redundant components, monitoring devices, or UAVs to help those CIs improve their resilience. In particular, the developed resilience index is able to account for the effect of CI components on the its probability of failure. Meanwhile, using contract theory, a comprehensive resource allocation framework is proposed enabling the system operator to optimally allocate resources to each individual CI based on its economic contribution to the entire system. Simulation results show that the system operator can economically benefit from allocating the resources while dams can have a significant improvement in their resilience indices. Subsequently, the developed contract-theoretic framework is extended to account for cases of asymmetric information in which the system operator has only partial information about the CIs being in some vulnerability and criticality levels. Under such asymmetry, it is shown that the proposed approach maximizes the system operator's utility while ensuring that no CI has an incentive to ask for another contract. Next, a proof-of-concept framework is introduced to analyze and improve the resilience of transportation networks against flooding. The effect of flooding on road capacities and on the free-flow travel time, is considered for different rain intensities and roads preparedness. Meanwhile, the total system's travel time before and after flooding is evaluated using the concept of a Wardrop equilibrium. To this end, a proactive mechanism is developed to reduce the system's travel time, after flooding, by shifting capacities (available lanes) between same road sides. In a nutshell, this dissertation provides a suite of analytical techniques that allow the optimization of security and resilience across multiple CPSs. Doctor of Philosophy Cyber-physical systems (CPSs) have recently been used in many application domains because of their ability to integrate physical elements with a cyber layer allowing for sensing, monitoring, and remote controlling. This pervasive use of CPSs in different applications has brought forward new security challenges and threats. Malicious attacks can now leverage the connectivity of the cyber layer to launch remote attacks and cause damage to the physical components. Taking these threats into consideration, it became imperative to ensure the security of CPSs. Given that many CPSs provide critical services, for instance many critical infrastructure (CI) are CPSs such as smart girds and nuclear reactors; it is then inevitable to ensure that these critical CPSs can maintain proper operation. One key measure of the CPS’s functionality, is resilience which evaluates the ability of a CPS to deliver its designated service under potentially disruptive situations. In general, resilience measures a CPS’s ability to adapt or rapidly recover from disruptive events. Therefore, it is crucial for CPSs to be resilient in face of potential failures. To this end, the central goal of this dissertation is to develop novel analytical frameworks that can evaluate and improve security and resilience of CPSs. In these frameworks, cross-disciplinary tools are used from game theory, contract theory, and optimization to develop robust analytical solutions for security and resilience problems. In particular, these frameworks led to the following key contributions in cyber security: developing an analytical framework to mitigate the effects of GPS spoofing attacks against UAVs, introducing a game-theoretic moving target defense (MTD) framework to improve the cyber security, and securing data privacy in m-health Internet of Things (IoT) networks using a MTD cryptographic framework. In addition, the dissertation led to the following contributions in CI resilience: developing a general framework using Bayesian Networks to evaluate and improve the resilience of CIs against their components failure, introducing a contract-theoretic model to allocate resources to multiple connected CIs under complete and asymmetric information scenarios, providing a proactive plan to improve the resilience of transportation networks against flooding, and, finally, developing an environment-aware framework to deploy UAVs in disaster-areas.

Traffic Collision Avoidance Systems (TCAS) are safety-critical systems that are deployed on most commercial aircraft in service today. However, TCAS transactions were not designed to account for malicious actors. While in the past it may have been infeasible for an attacker to craft arbitrary radio signals, attackers today have access to open-source digital signal processing software like GNU Radio and inexpensive Software Define Radios (SDR). Therefore, this thesis presents motivation through analytical and experimental means for more investigation into TCAS from a security perspective. Methods for analyzing TCAS both qualitatively and quantitatively from an adversarial perspective are presented, and an experimental attack is developed in GNU Radio to perform an attack in a well-defined threat model. Master of Science Since 1993, the Federal Aviation Administration (FAA) requires that many commercial turbine-powered aircraft to be outfitted with an on-board mid-air collision mitigation system. This system is known as the Traffic Collision Avoidance System (TCAS) in the United States, and it is known as the Airborne Collision Avoidance System (ACAS) in other parts of the world. TCAS/ACAS is a type of safety-critical system, which means that implementations need to be highly tolerant to system failures because their operation directly affects the safety of the on-board passengers and crew. However, while safety-critical systems are tolerant to failures, the designers of these systems only account for failures that occur in a cooperative environment; these engineers fail to account for “bad actors” who want to attack the weaknesses of these systems, or they assume that attacking such a system is infeasible. Therefore, to demonstrate how safety-critical systems like TCAS/ACAS are vulnerable to such bad actors, this thesis presents a method for manipulating the TCAS/ACAS in the favor of a bad actor. To start, a method for qualitatively and quantitatively analyzing the system’s vulnerabilities is presented. Then, using Software Defined Radio (SDR), which is a free and open-source effort to combine the flexibility of software with the power of wireless communication, this thesis shows how an actor can craft wireless signals such that they appear to look like an aircraft on a collision course with a target.

Real-Time systems are defined not only by their logical correctness but also timeliness. Modern real-time systems, such as those controlling industrial plants or the flight controller on UAVs, are no longer isolated. The same computing resources are shared with a variety of other systems and software. Further, these systems are increasingly being connected and made available over the internet with the rise of Internet of Things and the need for automation. Many real-time systems contain sensitive code and data, which not only need to be kept confidential but also need protection against unauthorized access and modification. With the cheap availability of hardware supported Trusted Execution Environments (TEE) in modern day microprocessors, securing sensitive information has become easier and more robust. However, when applied to real-time systems, the overheads of using TEEs make scheduling untenable. However, this issue can be mitigated by judiciously utilizing TEEs and capturing TEE operation peculiarities to create better scheduling policies. This thesis provides a new task model and scheduling approach, Split-TEE task model and a scheduling approach ST-EDF. It also presents simulation results for 2 previously proposed approaches to scheduling TEEs, T-EDF and CT-RM. Master of Science Real-Time systems are computing systems that not only maintain the traditional purpose of any computer, i.e, to be logically correct, but also timeliness, i.e, guaranteeing an output in a given amount of time. While, traditionally, real-time systems were isolated to reduce interference which could affect the timeliness, modern real-time systems are being increasingly connected to the internet. Many real-time systems, especially those used for critical applications like industrial control or military equipment, contain sensitive code or data that must not be divulged to a third party or open to modification. In such cases, it is necessary to use methods to safeguard this information, regardless of the extra processing time/resource consumption (overheads) that it may add to the system. Modern hardware support Trusted Execution Environments (TEEs), a cheap, easy and robust mechanism to secure arbitrary pieces of code and data. To effectively use TEEs in a real-time system, the scheduling policy which decides which task to run at a given time instant, must be made aware of TEEs and must be modified to take as much advantage of TEE execution while mitigating the effect of its overheads on the timeliness guarantees of the system. This thesis presents an approach to schedule TEE augmented code and simulation results of two previously proposed approaches.

Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to queue spillbacks, which disrupt traffic flows across the entire network and increase congestion. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This manuscript presents a technology-agnostic adaptive real-time server based approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide timeliness guarantee bounds by analyzing the travel time delays, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed with robots mimicking human driving behaviors. Compared to the pre-timed traffic control and an adaptive scheduling based traffic control, our algorithm is able to avoid spillbacks under highly dynamic traffic conditions and improve the average crossing delay in most cases by 10--50 %. Master of Science Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to congestion that blocks other intersection upstream, which disrupt traffic flows across the entire network. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This work presents a technologyagnostic adaptive approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide theoretical bounds to guarantee the performance of our approach in terms of the travel delays that may incur on the vehicles in the system, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed which uses robots to mimic human driving behaviour in an urban environment. Comparisons with widely deployed and state-of-the-art traffic control techniques show that our approach is able to minimize spillbacks as well as improve on the average crossing delay in most cases.

Engine-triggered tasks are real-time tasks that are released when the crankshaft arrives at certain positions in its path of rotation. This makes the rate of release of these jobs a function of the crankshaft's angular speed and acceleration. In addition, several properties of the engine triggered tasks like the execution time and deadlines are dependent on the speed profile of the crankshaft. Such tasks are referred to as adaptive-variable rate (AVR) tasks. Existing methods to calculate the worst-case demand of AVR tasks are either inaccurate or computationally intractable. We propose a method to efficiently calculate the worst-case demand of AVR tasks by transforming the problem into a variant of the knapsack problem. We then propose a framework to systematically narrow down the search space associated with finding the worst-case demand of AVR tasks. Experimental results show that our approach is at least 10 times faster, with an average runtime improvement of 146 times for randomly generated task sets when compared to the state-of-the-art technique. Master of Science Real-time systems require temporal correctness along with accuracy. This notion of temporal correctness is achieved by specifying deadlines to each of the tasks. In order to ensure that all the deadlines are met, it is important to know the processor requirement, also known as demand, of a task over a given interval. For some tasks, the demand is not constant, instead it depends on several external factors. For such tasks, it becomes necessary to calculate the worst-case demand. Engine-triggered tasks are activated when the crankshaft in an engine is at certain points in its path of rotation. This makes their activation rate dependent on the angular speed and acceleration of the crankshaft. In addition, several properties of the engine triggered tasks like the execution time and deadlines are dependent on the speed profile of the crankshaft. Such tasks are referred to as adaptive-variable rate (AVR) tasks. Existing methods to calculate the worst-case demand of AVR tasks are either inaccurate or computationally intractable. We propose a method to efficiently calculate the worst-case demand of AVR tasks by transforming the problem into a variant of the knapsack problem. We then propose a framework to systematically narrow down the search space associated with finding the worst-case demand of AVR tasks. Experimental results show that our approach is at least 10 times faster, with an average runtime improvement of 146 times for randomly generated task sets when compared to the state-of-the-art technique.

The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. A cost-effective approach for improving and optimizing transportation-related problems is to unlock hidden knowledge in ever-increasing spatiotemporal and crowdsourced information collected from various sources such as mobile phone sensors (e.g., GPS sensors) and social media networks (e.g., Twitter). Data mining and machine learning techniques are the major tools for analyzing the collected data and extracting useful knowledge on traffic conditions and mobility behaviors. Deep learning is an advanced branch of machine learning that has enjoyed a lot of success in computer vision and natural language processing fields in recent years. However, deep learning techniques have been applied to only a small number of transportation applications such as traffic flow and speed prediction. Accordingly, my main objective in this dissertation is to develop state-of-the-art deep learning architectures for resolving the transport-related applications that have not been treated by deep learning architectures in much detail, including (1) travel mode detection, (2) vehicle classification, and (3) traffic information system. To this end, an efficient representation for spatiotemporal and crowdsourced data (e.g., GPS trajectories) is also required to be designed in such a way that not only be adaptable with deep learning architectures but also contains efficient information for solving the task-at-hand. Furthermore, since the good performance of a deep learning algorithm is primarily contingent on access to a large volume of training samples, efficient data collection and labeling strategies are developed for different data types and applications. Finally, the performance of the proposed representations and models are evaluated by comparing to several state-of-the-art techniques in literature. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application. PHD The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. Furthermore, the recent advances in positioning tools (e.g., GPS sensors) and ever-popularity of social media networks have enabled generation of massive spatiotemporal and crowdsourced data. This dissertation aims to leverage the advances in artificial intelligence so as to unlock the rick knowledge in the recorded data and in turn, optimizing the transportation systems in a cost-effective way. In particular, this dissertation seeks for proposing end-to-end frameworks based on deep learning models, as an advanced branch of artificial intelligence, as well as spatiotemporal and crowdsourced datasets (e.g., GPS trajectory and social media) for improving three transportation problems. (1) Travel Mode Detection, which is defined as identifying users’ transportation mode(s) (e.g., walk, bike, bus, car, and train) when traveling around the traffic network. (2) Vehicle Classification, which is defined as identifying the vehicle’s type (e.g., passenger car and truck) while moving in a traffic network. (3) traffic information system based on social media networks, which is defined as detecting traffic events (e.g., crash) and capturing traffic information (e.g., traffic congestion) on a real-time basis from users’ tweets. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application.

Smart building's benefits range from improving comfort of occupant, increased productivity, reduction in energy consumption and operating costs, lower CO2 emission, to improved life cycle of utilities, efficient operation of building systems, etc. [65]. Hence, modern building owners are turning towards smart buildings. However, the current smart buildings mostly are not capable of achieving the objectives they are designed for and they can improve a lot better [22]. Therefore, a new technology called, Internet of Things, or IoT, is combined with the smart buildings to improve their performance [23]. IoT is the inter-networking of things embedded with electronics, software, sensors, actuators, and network connectivity to collect and exchange data, and things in this definition is anything and everything around us and even ourselves. Using this technology, e.g. a door can be a thing and can sense how many people have passed it's sensor to enter a space and let the lighting system know to prepare appropriate amount of light, or the HVAC (Heating Ventilation Air Conditioning) system to provide desirable temperature. IoT will provide a lot of useful information that before that accessibility to it was impossible, e.g., condition of water pipes in winter, which helps avoiding damages like frozen or broken pipes. However, despite all the benefits, IoT suffers from being vulnerable to cyber attacks. Examples have been provided later in Chapter 1. In this project among building systems, HVAC system is chosen to be automated with a new control method called MPC (Model Predictive Control). This method is fast, very energy efficient and has a lower than 0.001 rate of error for regulating the space temperature to any temperature that the occupants desire according to the results of this project. Furthermore, a PID (Proportional–Integral–Derivative) controller has been designed for the HVAC system that in the exact same cases MPC shows a much better performance. To design controllers for HVAC system and set the temperature to the desired value a method to automate balancing the heat flow should be found, therefore a thermal model of building should be available that using this model, the amount of heat, flowing in and out of a space in the building disregarding the external weather would be known to estimate. To automate the HVAC system using the programming languages like MATLAB, there is a need to convert the thermal model of the building to a mathematical model. This mathematical model is unique for each building depending on how many floors it has, how wide it is, and what materials have been used to construct the building. This process is needs a lot of effort and time even for buildings with 2 floors and 2 rooms on each floor and at the end the engineer might have done it with error. In this project you will see a software that will do the conversion of thermal model of buildings in any size to their mathematical model automatically, which helps improving the HVAC controllers to set temperature to the value occupants desire and avoid errors and time loss which is put both into calculations and troubleshooting. In addition, a test environment has been designed and constructed as a cyber physical system that allows us to test the IoT- enabled control systems before implementing them on real buildings, observe the performance, and decide if the system is satisfying or not. Also, all cyber threats can be explored and the solutions to those attacks can be evaluated. Even for the systems that are already out there, there is an opportunity to be assessed on this testbed and if there is any vulnerability in case of cyber security, solutions would be evaluated and help the existing systems improve. Master of Science Buildings function as shelters more than any thing else, and this has allowed humans to use it as a space to store important things like private and important information. Therefore, this space should be safe and secure from any vulnerabilities for occupants and their information. Smart buildings, have made a great difference in increasing the comfort level of occupants, but they haven’t been greatly successful achieving their objectives [50]. Therefore, a new technology called, Internet of Things, or IoT, is combined with the smart buildings to improve their performance [23]. IoT is the inter-networking of things embedded with electronics, software, sensors, actuators, and network connectivity to collect and exchange data, and things in this definition is anything and everything around us and even ourselves. Internet of Things (IoT) has helped improving the smart buildings and getting a considerable amount of energy efficiency [27]. But adding Internet of Things has added a network of things connected to internet, which gives the cyber hackers an opportunity to hack the buildings, and get access to the information stored inside the building or put even occupants lives in danger. Therefore, in this thesis the following items have been contributed: • Designing and programming a novel control system for HVAC system of the buildings (Model Predictive Control): This is a new method to control HVAC system of buildings and in comparison with the methods available in the market, it is the most energy efficient, it is faster, and it has a lower error rate in following the desired temperature of the occupants. • Design and construction of IoT- enabled smart building testbed: Since cyber attacks make buildings vulnerable, the author believes it is better to build a test environment to simulate the buildings and the control methods that are used inside the buildings, and try to evaluate performance of the control methods before implementing them on real buildings. Also, by installing IoT sensors inside the test environment, the engineers can perform some cyber attack tests, and also evaluate the solutions for each attack on the testbed. • Design and program a software to convert thermal model of buildings to mathematical model : In designing a new control method for HVAC system of buildings, the first required information is the thermal model of the buildings. Eventually, there is a need to program. Thus, the thermal model should be converted to a mathematical model. However, there is a heavy manual calculation behind it that is really overwhelming, tiring, with a high possibility of error, and time-consuming even for a very small sized building. Therefore, automating this process in terms of a software that takes the information of thermal model of buildings as an input and giving the output of the mathematical model of building is a considerable achievement.

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle's onboard unit (OBU). The main research question asks: 'What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?' The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network's probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models. Ph. D. The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle’s onboard unit (OBU). The main research question asks: “What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?” The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network’s probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models.

Modern innovation is a driving force behind increased spectrum crowding. Several studies performed by the National Telecommunications and Information Administration (NTIA), Federal Communications Commission (FCC), and other groups have proposed Dynamic Spectrum Access (DSA) as a promising solution to alleviate spectrum crowding. The spectrum assignment decisions in DSA will be made by a centralized entity referred to as as spectrum access system (SAS); however, maintaining spectrum utilization information in SAS presents privacy risks, as sensitive Incumbent User (IU) operation parameters are required to be stored by SAS in order to perform spectrum assignments properly. These sensitive operation parameters may potentially be compromised if SAS is the target of a cyber attack or an inference attack executed by a secondary user (SU). In this thesis, we explore the operational security of IUs in SAS-based DSA systems and propose a novel privacy-preserving SAS-based DSA framework, Suspicion Zone SAS (SZ-SAS), the first such framework which protects against both the scenario of inference attacks in an area with sparsely distributed IUs and the scenario of untrusted or compromised SAS. We then define modifications to the SU inference attack algorithm, which demonstrate the necessity of applying obfuscation to SU query responses. Finally, we evaluate obfuscation schemes which are compatible with SZ-SAS, verifying the effectiveness of such schemes in preventing an SU inference attack. Our results show SZ-SAS is capable of utilizing compatible obfuscation schemes to prevent the SU inference attack, while operating using only homomorphically encrypted IU operation parameters. Master of Science Dynamic Spectrum Access (DSA) allows users to opportunistically access spectrum resources which were previously reserved for use by specified parties. This spectrum sharing protocol has been identified as a potential solution to the issue of spectrum crowding. This sharing will be accomplished through the use of a centralized server, known as a spectrum access system (SAS). However, current SAS-based DSA proposals require users to submit information such as location and transmission properties to SAS. The privacy of these users is of the utmost importance, as many existing users in these spectrum bands are military radars and other users for which operational security is pivotal. Storing the information for these users in a central database can be an major privacy issue, as this information could be leaked if SAS is compromised by a malicious party. Additionally, malicious secondary users (SUs) may perform an inference attack, which could also reveal the location of these military radars. In this thesis, we demonstrate a SAS-framework, SZ-SAS, which allows SAS to function without direct knowledge of user information. We also propose techniques for mitigating the inference attack which are compatible with SZ-SAS

A new class of Wireless Sensor Networks (WSNs) is emerging within the Internet of Things (IoT) that features extremely high node density, low data rates per node, and high network dependability. Applications such as industrial IoT, factory automation, vehicular networks, aviation, spacecraft and others will soon feature hundreds of low power, low data rate (1-15 kbps) wireless sensor nodes within a limited spatial environment. Existing Medium Access Control (MAC) layer protocols, namely IEEE 802.15.4, may not be suitable for highly dense, low rate networks. A new MAC protocol has been proposed that supports a Receiver-Assigned Code Division Multiple Access (RA-CDMA) physical (PHY) layer multiple access technique, which may enable higher network scalability while maintaining performance and contributing additional robustness. This thesis presents a comparison of the contention mechanisms of IEEE 802.15.4 non- beacon enabled mode and RA-CDMA along with a Matlab simulation framework used for end-to-end simulations of the protocols. Simulations suggest that IEEE 802.15.4 networks begin to break down in terms of throughput, latency, and delivery ratio at a relatively low overall traffic rate compared to RA-CDMA networks. Results show that networks using the proposed RA-CDMA multiple access can support node densities on the order of two to three times higher than IEEE 802.15.4 within the same bandwidth. Furthermore, features of a new MAC layer protocol are proposed that is optimized for RA-CDMA, which could further improve network performance over IEEE 802.15.4. The protocol's simple and lightweight design eliminates significant overhead compared to other protocols while meeting performance requirements, and could further enable the deployment of RA-CDMA WSNs. Master of Science

In the last two-decades data center networks have evolved to become a key element in improving levels of productivity and competitiveness for different types of organizations. Traditionally data center networks have been constructed with 3 layers of switches, Edge, Aggregation, and Core. Although this Three-Tier architecture has worked well in the past, it poses a number of challenges for current and future data centers. Data centers today have evolved to support dynamic resources such as virtual machines and storage volumes from any physical location within the data center. This has led to highly volatile and unpredictable traffic patterns. Also The emergence of "Big Data" applications that exchange large volumes of information have created large persistent flows that need to coexist with other traffic flows. The Three-Tier architecture and current routing schemes are no longer sufficient for achieving high bandwidth utilization. Data center networks should be built in a way where they can adequately support virtualization and cloud computing technologies. Data center networks should provide services such as, simplified provisioning, workload mobility, dynamic routing and load balancing, equidistant bandwidth and latency. As data center networks have evolved the Three-Tier architecture has proven to be a challenge not only in terms of complexity and cost, but it also falls short of supporting many new data center applications. In this work we propose OneSwitch: A switch architecture for the data center. OneSwitch is backward compatible with current Ethernet standards and uses an OpenFlow central controller, a Location Database, a DHCP Server, and a Routing Service to build an Ethernet fabric that appears as one switch to end devices. This allows the data center to use switches in scale-out topologies to support hosts in a plug and play manner as well as provide much needed services such as dynamic load balancing, intelligent routing, seamless mobility, equidistant bandwidth and latency. PHD

Embedded systems are increasingly ubiquitous. Many of them have security requirements such as smart cards, mobile phones, and internet connected appliances. It can be a challenge to fulfill security requirements due to the constrained nature of embedded devices. This security challenge is worsened by the possibility of implementation attacks. Despite well formulated cryptosystems being used, the underlying hardware can often undermine any security proven on paper. If a secret key is at play, an adversary has a chance of revealing it by simply looking at the power variation. Additionally, an adversary can tamper with an embedded system's environment to get it to skip a security check or generate side channel information. Any adversary with physical access to an embedded system can conduct such implementation attacks. It is the focus of this work to explore different countermeasures against both side channel and fault attacks. A new countermeasure call Intra-instruction Redundancy, based on bit-slicing, or N-bit SIMD processing, is proposed. Another challenge with implementing countermeasures against implementation attacks, is that they need to be able to be combined. Most proposed side channel countermeasures do not prevent fault injection and vice versa. Combining them is non-trivial as demonstrated with a combined implementation attack. Master of Science