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This work introduces a generic, machine learning (ML)-based platform for functional reverse engineering (RE) of circuits. Our proposed platform GNN-RE leverages the notion of graph neural networks (GNNs) to (i) represent and analyze flattened/ unstructured gate-level netlists, (ii) automatically identify the boundaries between the modules or sub-circuits implemented in such netlists and (iii) classify the sub-circuits based on their functionalities. For GNNs in general, each graph node is tailored to learn about its own features and its neighboring nodes, which is a powerful approach for the detection of any kind of sub-graphs of interest. For GNN-RE, in particular, each node represents a gate and is initialized with a feature vector that reflects on the functional and structural properties of its neighboring gates. GNN-RE also learns the global structure of the circuit, which facilitates identifying the boundaries between subcircuits in a flattened netlist. Initially, to provide high-quality data for training of GNN-RE, we deploy a comprehensive dataset of foundational designs/components with differing functionalities, implementation styles, bit-widths, and interconnections. GNN-RE is then tested on the unseen shares of this custom dataset, as well as the EPFL benchmarks, the ISCAS-85 benchmarks, and the 74X series benchmarks. GNN-RE achieves an average accuracy of 98:82% in terms of mapping individual gates to modules, all without any manual intervention or post-processing. We also release our code and source data 1.

Side-channel attack (SCA) is regarded as a sig- nificant risk to the hardware implementation of cryptographic systems. Side-channel information, such as timing, power, and electromagnetic radiation, is leaked through the system and can be exploited for secret key extraction. The work proposes a real- time and compatible detection method for power SCAs. The technique makes use of a switched capacitor DC-DC (SC-DCDC) converter along with a lightweight artificial intelligence engine for power SCA detection. The proposed system, referred to as EoH, has the ability to perform dynamic voltage scaling and learn the behaviors of the cryptographic system to identify any potential attacks. The switching activities of the SC-DCDC converter can be viewed as a measurement of the cryptographic function. Thus, the recurrent neural network was chosen as it best processes timeseries data. The technique is system-specific, meaning that during the enrollment phase, the normal operation of the system is learned. The technique can also be expanded to include other types of SCA and is not limited to power.

The wireless networks of the future are evolving to enable reliable communication for resource-constrained, miniaturized internet of things (IoT) devices, which will place stringent demands on future sixth-generation (6G) mobile networks. These demands include low cost, very low latency, improved spectrum, and power efficiency, higher reliability, and significantly improved data rates. Emphasizing that these devices have limited functionality and may be placed in inaccessible locations, replacing or recharging batteries can be a daunting task, so energy-efficient solutions should be developed to ensure uninterrupted, seamless wireless communication for the power-constrained IoT devices. In this paper, we consider the integration of long-range (LoRa) modulation into backscatter communications (BackCom), and we develop a mathematical framework in order to investigate the error rate performance of the considered system model. In particular, we derive novel exact and approximated closed-form expressions for the symbol error rate (SER), under the assumption of canceled radio-frequency (RF) interference. The obtained analytical results, corroborated by numerical results, confirm the advantages of integrating LoRa into BackCom system as a low-complex technique in order to extend the transmission distance in power-limited backscatter devices.

Hybrid precoding has been envisaged as an attractive alternative to fully digital precoding in massive multiple-input-multiple-output (mMIMO) systems, where it can ultimately reduce the cost and the power consumption while maintaining an acceptable sum rate. Within this context, deep learning (DL) is proposed as an optimization tool to realize this. However, most of the existing DL based hybrid precoding solutions are based on the convolutional neural networks (CNNs), which have inductive bias that limits the learning capability of the highly stochastic massive MIMO channel. Conversely, the present contribution proposes a novel DL based hybrid precoder that can achieve an improved performance at a reduced computation time compared to the CNN based techniques. This is achieved through the design of a Perceiver Neural Network (PNN) architecture for hybrid precoding, where the PNN accepts a noisy channel matrix as an input and produces the analog precoder and combiners as an output. Also, the proposed architecture learns to reshape the input data in order to achieve the best accuracy through a novel trainable reshaping module. Our design involves an offline phase, in which we build our realistic ray tracing based massive MIMO dataset to train our Perceiver-based hybrid precoder (PBHP). In this context, we conduct extensive experiments to compare the PBHP with a CNN-based hybrid precoder (CNN-HP). It is extensively shown that the proposed PBHP outperforms the CNN-HP in terms of both accuracy and inference time. Moreover, the PBHP is more robust when the transmit power is low and the number of antennas is large. Finally, the offered results demonstrate that the PBHP exhibits a drastically less inference time (by nearly an order of magnitude) than the CNN-HP, especially for higher number of antennas, rendering it a promising candidate for the next-generation wireless networks.

Convolutional Neural Networks (CNNs) revolutionized computer vision and reached the state-of-the-art performance for image processing, object recognition, and video classification. Even though CNN inference is notoriously compute-intensive, as convolutions account for >90% of the total operation tasks, the ability to tradeoff between accuracy, performance, power, and latency to meet target application makes it an open research topic. This paper proposes the Spatial Locality Input Data (SLID) method for computational reuse during the inference stage for a pre-trained network. The method exploits input data spatial locality via skipping partial processing of the multiply-and-accumulate (MAC) operations for adjacent data and equating its value to previously computed ones. SLID improves the throughput of resource-constrained devices (Internet-of-Things, edge devices) and accelerates computations during the inference phase by reducing the number of MAC operations. Such approximate computing schema does not require a similarity quantification step nor any modification for the training stage. The computational data reuse was evaluated on three well-known distinctive CNN structures and data sets with alternating layer selections: LeNet, CIFAR-10, and AlexNet. The computational data reuse method saves up to 34.9%, 49.84%, and 31.5% of MAC operations while reducing the accuracy by 8%, 3.7%, and 5.0% for the three models mentioned earlier, respectively. Besides, the proposed method saves on memory access by eliminating data fetching of skipped inputs. Furthermore, filter size, strides, and padding on the accuracy and savings of operations are analyzed. SLID is the first work to exploit the input spatial locality for savings on CNN convolution operations with minimal accuracy loss and without memory or computational overhead. This makes it a great option to support intelligence at the edge.

The emergence of Internet-of-Things (IoT) has led to the development of new energy efficient and long-range wireless technologies, which are necessary for the successful realization of IoT applications. Within this context, long range (LoRa) has emerged as one of the prominent low power wide area network (LPWAN) technologies that is envisioned to accommodate the future IoT requirements. However, current LoRa frameworks suffer from limited network capacity, particularly in dense deployments. Consequently, power domain-superposition modulation (PD-SPM) is integrated with LoRa to alleviate the aforementioned problem. In this paper, the error rate performance of a LoRa-enabled PD-SPM system is investigated over Rayleigh fading channels. Specifically, symbol error rate (SER) expressions are derived to characterize the performance of an arbitrary number of users under extreme fading conditions. Furthermore, Monte Carlo simulations are presented to validate the analytical framework.

The increased proliferation of connected devices requires a paradigm shift towards the development of innovative technologies for the next generation of wireless systems. One of the key challenges, however, is the spectrum scarcity, owing to the unprecedented broadband penetration rate in recent years. Visible light communications (VLC) has recently emerged as a possible solution to enable high-speed short-range communications. However, VLC systems suffer from several limitations, including the limited modulation bandwidth of light-emitting diodes. Consequently, several multiple access techniques (MA), e.g., space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA), have been considered for VLC networks. Despite their promising multiplexing gains, their performance is somewhat limited. In this article, we first provide an overview of the key MA technologies used in VLC systems. Then, we introduce rate-splitting multiple access (RSMA), which was initially proposed for RF systems and discuss its potentials in VLC systems. Through system modeling and simulations of an RSMA-based two-use scenario, we illustrate the flexibility of RSMA in generalizing NOMA and SDMA, as well as its superiority in terms of weighted sum rate (WSR) in VLC. Finally, we discuss challenges, open issues, and research directions, which will enable the practical realization of RSMA in VLC., 19 pages, 14 figures. arXiv admin note: substantial text overlap with arXiv:2002.07583

Binary convolutional neural networks (BCNN) have been proposed in the literature for resource-constrained IoTs nodes and mobile computing devices. Such computing platforms have strict constraints on the power budget, system performance, processing and memory capabilities. Nonetheless, the platforms are still required to efficiently perform classification and matching tasks needed in various applications. The memristor device has shown promising results when utilized for in-memory computing architectures, due to its ability to perform storage and computation using the same physical element. This is in addition to their nonlinear dynamic characteristics that can be used for various applications. Emulating the memristor operation through FPGAs implementation provides flexibility for rapid prototyping and exploration of the design space. In this paper, a FPGA-based memristor emulator circuit is implemented using a multi-bit XNOR gate as the main block for binary convolution which is followed by a memristor-based pooling layer. The BCNN layer is realized by bitwise XNOR-cell followed by wide NOR gate. The implementation has been successfully synthesized and verified using Xilinx Nexys4 FPGA with less than 1% utilization and 144.9 MHz frequency of operation. In order to test the functionality of the proposed cores, a digital implementation for the circuit was realized and verified experimentally. The experimental results show similar performance when compared to software simulation.

Cooperative communications is a core research area in wireless vehicular networks (WVNs), thanks to its capability to mitigate fading and improve spectral efficiency. In a cooperative scenario, the performance of the system is improved by selecting the best relay for data transmission among a group of available relays. However, due to the mobility of WVNs, the best relay is often selected in practice based on outdated channel state information (CSI), which in turn affects the overall system performance. Therefore, there is a need for a robust relay selection scheme (RSS) that improves the overall achievable performance of an outdated CSI. Motivated by this and considering the advantageous features of autoregressive moving average (ARMA), in the present work we model a cooperative vehicular communication scenario with relay selection as a Markov decision process (MDP) and propose two deep Q-networks (DQNs), namely DQN-RSS and DQN-RSS-ARMA. In the proposed framework, two deep reinforcement learning (RL)-based RSS are trained based on the intercept probability, aiming to select the optimal vehicular relay from a set of multiple relays. We then compare the proposed RSS with the conventional methods and evaluate the performance of the network from the security point of view. Simulation results show that DQN-RSS and DQN-RSS-ARMA perform better than conventional approaches, and they reduce intercept probability by approximately 15\% and 30\%, respectively, compared to the ARMA approach.

Visible light communication is envisaged as a promising enabling technology for sixth generation (6G) and beyond networks. It was introduced as a key enabler for reliable massive-scale connectivity, mainly thanks to its simple and low-cost implementation which require minor variations to the existing indoor lighting systems. The key features of VLC allow offloading data traffic from the current congested radio frequency (RF) spectrum in order to achieve effective short-range, high speed, and green communications. However, several challenges prevent the realization of the full potentials of VLC, namely the limited modulation bandwidth of light emitting diodes, the interference resulted from ambient light, the effects of optical diffuse reflection, the non-linearity of devices, and the random receiver orientation. Meanwhile, centralized machine learning (ML) techniques have exhibited great potentials in handling different challenges in communication systems. Specifically, it has been recently shown that ML algorithms exhibit superior capabilities in handling complicated network tasks, such as channel equalization, estimation and modeling, resources allocation, opportunistic spectrum access control, non-linearity compensation, performance monitoring, detection, decoding/encoding, and network optimization. Nevertheless, concerns relating to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in large-scale implementation of centralized ML techniques. This has motivated the emergence of a new distributed ML paradigm, namely federated learning (FL). This method can reduce the cost associated with transferring the raw data, and preserve clients privacy by training ML model locally and collaboratively at the clients side. Thus, the integration of FL in VLC networks can provide ubiquitous and reliable implementation of VLC systems. Based on this, for the first time in the open literature, we provide an overview about VLC technology and FL. Then, we introduce FL and its integration in VLC networks and provide an overview on the main design aspects. Finally, we highlight some interesting future research directions of FL that are envisioned to boost the performance of VLC systems.

Memristor characteristics such as nonlinear dynamics, state retention and accumulation are useful for many applications. FPGA implementation of memristor-based systems and algorithms provides fast development and verification platform. In this work, we first propose a versatile digital memristor emulator that exhibits either continuous or discrete behaviors, similar to valence change memories (VCM) and the electrochemical metallization memories. Secondly, the proposed memristor emulator is used to design a chaotic generator circuit utilizing the memristor's nonlinearity. Finally, the chaotic system is used to design a speech encryption engine to demonstrate its capabilities. The memristor emulator, chaotic generator, and the encryption system were implemented on Nexys 4 Artix-7 FPGA XC7A100T. The implementation results show an efficiency in throughput and hardware resources utilization compared to the previous works. In addition, the encryption system results show good performance against several perceptual, statistical attacks in addition to resistance to security attacks tests including differential attacks, NIST tests, key space analysis, mean square error (MSE), correlation, histogram and spectrogram.

Mobile agents (MAs) are being widely used in distributed applications development. The motivation behind the interest in MAs is derived from the various advantages they offer, such as, autonomous behavior, mobility and intelligence. Also, their small size and requirement of a low bandwidth are other attractive features. However, the dynamic behavior of agents and hosts in Mobile Agent Systems (MASs) has posed a challenging problem. Maintaining good performance is important for MASs to guarantee the quality of provided services. To address both of these issues we propose a new adaptive trust and reputation model for MASs. The proposed model provides users with the means to assess service providers and decision making basis on who to interact with. It combines direct and indirect witnesses' experience evaluations. It also assesses the honesty of witnesses to filter out false evaluations. In addition, new “Incentive and Penalty” and “Second Chance” approaches are incorporated into the model to motivate an honest behavior and accommodate changes in the system. A testbed is conducted to show how the system adapts to changes in witnesses' behavior. Also a framework for comparison is also developed to evaluate and compare the proposed model and compare it to other existing models found in the literature.

Visible light communication is envisaged as a promising enabling technology for sixth generation (6G) and beyond networks. It was introduced as a key enabler for reliable massive-scale connectivity, mainly thanks to its simple and low-cost implementation which require minor variations to the existing indoor lighting systems. The key features of VLC allow offloading data traffic from the current congested radio frequency (RF) spectrum in order to achieve effective short-range, high speed, and green communications. However, several challenges prevent the realization of the full potentials of VLC, namely the limited modulation bandwidth of light emitting diodes, the interference resulted from ambient light, the effects of optical diffuse reflection, the non-linearity of devices, and the random receiver orientation. Meanwhile, centralized machine learning (ML) techniques have exhibited great potentials in handling different challenges in communication systems. Specifically, it has been recently shown that ML algorithms exhibit superior capabilities in handling complicated network tasks, such as channel equalization, estimation and modeling, resources allocation, opportunistic spectrum access control, non-linearity compensation, performance monitoring, detection, decoding/encoding, and network optimization. Nevertheless, concerns relating to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in large-scale implementation of centralized ML techniques. This has motivated the emergence of a new distributed ML paradigm, namely federated learning (FL). This method can reduce the cost associated with transferring the raw data, and preserve clients privacy by training ML model locally and collaboratively at the clients side. Thus, the integration of FL in VLC networks can provide ubiquitous and reliable implementation of VLC systems. Based on this, for the first time in the open literature, we provide an overview about VLC technology and FL. Then, we introduce FL and its integration in VLC networks and provide an overview on the main design aspects. Finally, we highlight some interesting future research directions of FL that are envisioned to boost the performance of VLC systems.

Visible light communication (VLC) has been emerged as a technology that can increase the channel capacity in the next generations of wireless technologies by exploiting the largely unutilized, licence-free and huge visible light portion of the electromagnetic spectrum. In order to enable high-speed short-range wireless communications, VLC utilizes the installed high-switching rate light emitting diodes (LEDs) in the ceilings of indoor environments, which are primarily used for illumination, to modulate the signals into visible light intensity. However, VLC suffers from several limitations, such as the limited modulation bandwidth and the coverage area of LEDs that degrade the overall system spectral efficiency (SE). In this respect, the present contribution proposes rate splitting multiple access (RSMA) for multi-cell indoor VLC systems as a mean to enhance the overall system SE and energy efficiency (EE) as well as to provide ubiquitous indoor coverage and to address user mobility issues. Moreover, we utilize coordinated beamforming to design the precoders of the common and the private streams in each cell aiming to enhance the performance of cell-edge users. Finally, the formulated sum of the mean squared error optimization problem is solved sub-optimally using an alternating optimization approach. Extensive computer simulations demonstrate that RSMA improves the overall system performance in terms of the SE and EE compared to the recently used multiple access techniques, such as space division multiple access with coordinated beamforming which constitutes a special case of it. acceptedVersion

Logic locking aims to protect the intellectual property (IP) of integrated circuit (IC) designs throughout the globalized supply chain. The SAIL attack, based on tailored machine learning (ML) models, circumvents combinational logic locking with high accuracy and is amongst the most potent attacks as it does not require a functional IC acting as an oracle. In this work, we propose UNSAIL, a logic locking technique that inserts key-gate structures with the specific aim to confuse ML models like those used in SAIL. More specifically, UNSAIL serves to prevent attacks seeking to resolve the structural transformations of synthesis-induced obfuscation, which is an essential step for logic locking. Our approach is generic; it can protect any local structure of key-gates against such ML-based attacks in an oracle-less setting. We develop a reference implementation for the SAIL attack and launch it on both traditionally locked and UNSAIL-locked designs. In SAIL, a change-prediction model is used to determine which key-gate structures to restore using a reconstruction model. Our study on benchmarks ranging from the ISCAS-85 and ITC-99 suites to the OpenRISC Reference Platform System-on-Chip (ORPSoC) confirms that UNSAIL degrades the accuracy of the change-prediction model and the reconstruction model by an average of 20.13 and 17 percentage points (pp) respectively. When the aforementioned models are combined, which is the most powerful scenario for SAIL, UNSAIL reduces the attack accuracy of SAIL by an average of 11pp. We further demonstrate that UNSAIL thwarts other oracle-less attacks, i.e., SWEEP and the redundancy attack, indicating the generic nature and strength of our approach. Detailed layout-level evaluations illustrate that UNSAIL incurs minimal area and power overheads of 0.26% and 0.61%, respectively, on the million-gate ORPSoC design., IEEE Transactions on Information Forensics and Security (TIFS)

This paper proposes a general framework for the energy efficiency analysis of a hybrid visible light communication (VLC) and Radio Frequency (RF) wireless system, in which both VLC and RF subsystems utilize non-orthogonal multiple access (NOMA) technology. The proposed framework is based on realistic communication scenarios as it takes into account the mobility of users and assumes imperfect channel-state information (CSI). In this context, tractable closed-form expressions are derived for the corresponding average sum-rate of NOMA-VLC and orthogonal frequency division multiple access (OFDMA)- VLC. It is shown extensively that incurred CSI errors have a considerable impact on the average energy efficiency of both NOMA-VLC and OFDMA-VLC systems. Hence, they should be taken into detailed account in the design of practical systems. We further demonstrate that the average energy efficiency of the hybrid NOMA-VLC-RF system outperforms the NOMA-VLC system under imperfect CSI. Respective computer simulations corroborate the derived analytic results, and interesting theoret- ical and practical insights are provided, which will be useful in the effective design and deployment of conventional VLC and hybrid VLC-RF systems publishedVersion

Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

Resistive RAM (RRAM) technologies are gaining importance due to their appealing characteristics, which include non-volatility, small form factor, low power consumption, and ability to perform logic operations in memory. These characteristics make RRAM highly suited for Internet of Things devices and similarly resource-constrained systems. This paper proposes a novel memristor-based xnor gate that enables the execution of xnor/xor function in the memristive crossbar memory. The proposed two-input xnor gate requires two steps to perform the xnor function. The design of the circuit utilizes bipolar and unipolar memristors and permits cascading by only adding an extra step and one computing memristor. To the best of our knowledge, this is the first native stateful xnor logic implementation. Spice simulations have been used to verify the functionality of the proposed circuit. This includes benchmarking the proposed design against the state-of-the-art stateful memristor-based logic circuits. The results for implementing three-input xor using the proposed circuit demonstrate efficient performance in terms of energy, latency, and area. The gate shows 56% saving in energy, 54% less number of steps (latency), and 50% less number of computing MR (area) compared with the state-of-the-art stateful xor/xnor implementations.

ElectroCardioGram(ECG) bio-sensors are used to detect and transmit the electrical activity of the heart over a period of time. The ECGs are treated by a signal processing unit inside the bio-sensor to detect and identify heart related medical conditions. Testing and verification of bio-sensor is of paramount importance as faults in these critical systems may lead to loss of life. However, due to the complexity of these systems traditional testing methods might not be adequate to provide high level of assurance about the correctness of their operation. In this paper, we propose a verification framework where we utilize simulation and formal methods to validate the correctness of the ECG bio-sensor. The framework is used to verify ECG bio-sensor at several levels of abstractions, including the signal processing level, where the ECG signal attributes, signal processing algorithm including its filters, detection and analysis algorithms, and triggering the alarm are modeled.

Mobile agents are smart programs that migrate from one platform to another to perform the user task. Mobile agents offer flexibility and performance enhancements to systems and service real-time applications. However, security in mobile agent systems is a great concern. In this paper, we propose a novel Broadcast based Secure Mobile Agent Protocol (BROSMAP) for distributed service applications that provides mutual authentication, authorization, accountability, nonrepudiation, integrity, and confidentiality. The proposed system also provides protection from man in the middle, replay, repudiation, and modification attacks. We proved the efficiency of the proposed protocol through formal verification with Scyther verification tool.

While financially advantageous, outsourcing key steps, such as testing, to potentially untrusted Outsourced Assembly and Test (OSAT) companies may pose a risk of compromising on-chip assets. Obfuscation of scan chains is a technique that hides the actual scan data from the untrusted testers; logic inserted between the scan cells, driven by a secret key, hides the transformation functions that map the scan-in stimulus (scan-out response) and the delivered scan pattern (captured response). While static scan obfuscation utilizes the same secret key, and thus, the same secret transformation functions throughout the lifetime of the chip, dynamic scan obfuscation updates the key periodically. In this paper, we propose ScanSAT: an attack that transforms a scan obfuscated circuit to its logic-locked version and applies the Boolean satisfiability (SAT) based attack, thereby extracting the secret key. We implement our attack, apply on representative scan obfuscation techniques, and show that ScanSAT can break both static and dynamic scan obfuscation schemes with 100% success rate. Moreover, ScanSAT is effective even for large key sizes and in the presence of scan compression., 16 pages, 14 figures, IEEE Transactions on Emerging Topics in Computing

The present work investigates the performance of non-orthogonal multiple access (NOMA) in a hybrid visible light communication (VLC) / radio frequency (RF) wireless network. In particular, we investigate the energy efficiency of the proposed architecture assuming imperfect channel state information (CSI), which is a realistic assumption that is encountered in practical indoor and outdoor wireless communication scenarios. We demonstrate that the performance of the proposed scheme in terms of energy efficiency outperforms by four-fold the corresponding performance of its orthogonal frequency division multiple access (OFDMA) counterpart. In addition, it is shown that the energy efficiency of the proposed scheme is more robust to CSI errors and line of sight (LOS) variations than the OFDMA-based scheme, which appears to be more susceptible to the CSI error and the LOS availability probability. Finally, our findings reveal that the performance gain of NOMA over OFDMA in the considered hybrid VLC/RF set up is directly proportional to the probability of LOS availability. These results are expected to be useful in the efficient design and efficient operation of hybrid VLC/RF wireless systems. acceptedVersion

Business Process Model and Notation (BPMN) is a standard graphical notation that is widely used for modeling Business Processes (BP) in Business Process Management (BPM) systems. A key application of such systems is continuous analysis of BP execution for checking compliance of execution logs with process models. In this paper we introduce a simple, human-readable rule language based on a fragment of First-Order Logic (FOL) and show how compliance rules can be generated directly from BPMN models. We focus on control flow aspects of BPMN models by (1) transforming the model to obtain a uniform representation of task activation (2) dividing the model into sets of components and (3) using our proposed language to generate compliance rules for each component. We show that these rules can be used in the analysis of the business process execution log using British Telecom’s Aperture business process analysis tool.

This article presents the architectures, theoretical analyses and testing results of modified time delay digital tanlock loop (TDTLs) system. The modifications to the original TDTL architecture were introduced to overcome some of the limitations of the original TDTL and to enhance the overall performance of the particular systems. The limitations addressed in this article include the non-linearity of the phase detector, the restricted width of the locking range and the overall system acquisition speed. Each of the modified architectures was tested by subjecting the system to sudden positive and negative frequency steps and comparing its response with that of the original TDTL. In addition, the performance of all the architectures was evaluated under noise-free as well as noisy environments. The extensive simulation results using MATLAB/SIMULINK demonstrate that the new architectures overcome the limitations they addressed and the overall results confirmed significant improvements in performance compared to the conventional TDTL system.

Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), A Machine Learning (ML) -based Intrusion Detection and Prevention System (IDPS) requires a large amount of labeled up-to-date training data, to effectively detect intrusions and generalize well to novel attacks. However, labeling of data is costly and becomes infeasible when dealing with big data, such as those generated by IoT (Internet of Things) -based applications. To this effect, building a ML model that learns from non- or partially-labeled data is of critical importance. This paper proposes a novel Semi-supervised Multi-Layered Clustering Model (SMLC) for network intrusion detection and prevention tasks. The SMLC has the capability to learn from partially labeled data while achieving a comparable detection performance to supervised ML-based IDPS. The performance of the SMLC is compared with well-known supervised ensemble ML models, namely, RandomForest, Bagging, and AdaboostM1 and a semi-supervised model (i.e., tri-training) on a benchmark network intrusion dataset, the Kyoto 2006+. Experimental results show that the SMLC outperforms all other models and can achieve better detection accuracy using only 20% labeled instances of the training data.

Automatic dependent surveillance-broadcast is an emerging surveillance technology for the future "e-enabled" aircrafts, which will make it possible for aircrafts to share their location data with neighboring aircrafts, ground controllers, and other interested parties. In order to provide the automatic dependent surveillance-broadcast communications with a high level of accuracy and integrity, a reliable authentication mechanism is required. So far, however, very few cryptographic solutions have been offered to achieve this in the literature. Even existing solutions have faced the following challenges: i the authentication solutions based on regular digital signature require complex management of public-key infrastructureell; and ii signing messages exchanged or broadcast frequently in aircraft-to-aircraft and aircraft-to-ground communication modes can cause a computational bottleneck easily. In order to address these challenges, we take a fresh approach to building up an authentication framework by introducing a new online/offline identity-based signature scheme. Our scheme will resolve the public-key infrastructure management issue by using the identities of aircrafts as public keys and will achieve a high efficiency through online/offline signature generation. Copyright © 2014 John Wiley & Sons, Ltd.

Hardware implementation of modern crypto devices paves the way for a special type of cryptanalysis, which is known as side channel analysis (SCA) attacks. These attacks are designed to extract critical information from the physical leakage of the digital circuitry such as the power consumption and electromagnetic emissions. Differential power analysis (DPA) attacks are considered the most efficient form of SCA attacks that require special types of countermeasures. Another form of attacks, known as fault analysis (FA), is based on forcing the circuit to produce faulty results in order to extract useful information about the secret. Several countermeasures have been proposed in the literature to address and mitigate SCA attacks at different levels of abstraction. They include algorithmic, gate and transistor-level countermeasures. Leakage originates at every level according to the implemented crypto system and attack methodology. Countermeasures at gate level and transistor level are more generic than those at the algorithmic level as they tend to be specialised for certain implementations. Complication of the design process increases down to the basic abstraction layers, however, gate-level countermeasures provide the balance of generality and design complication. The major state-of-the-art gate-level countermeasures against DPA and FA attacks are reviewed here.

This paper presents the design, analysis, simulation and implementation of the architecture of a new non-uniform type digital phaselocked loop (DPLL). The proposed loop uses a composite phase detector (CPD), which consists of a sample-and-hold unit and an arctan block. The CPD improves the system linearity and results in a wider lock range. In addition, the loop has an adaptive controller block, which can be used to minimize the overall system sensitivity to variations in the power of the input signal. Furthermore, the controller has a tuning mechanism that gives the designer the flexibility to customize the loop parameters to suit a particular application. These performance parameters include lock range, acquisition time, phase noise or jitter, and signal to noise ratio enhancement. The simulation results show that the proposed loop provides flexibility to optimize the major conflicting system parameters. A prototype of the proposed system was implemented using an FPGA and the practical results concur with those obtained by simulation using MATLAB/Simulink.

Memristors are one of the emerging technologies that can potentially replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. Over the past few years, research and development mostly focused on revolutionizing the metal oxide materials, which are used as core components of the popular metal-insulator-metal memristors owing to their highly recognized resistive switching behavior. This paper outlines the recent advancements and characteristics of such memristive devices, with a special focus on (i) their established resistive switching mechanisms and (ii) the key challenges associated with their fabrication processes including the impeding criteria of material adaptation for the electrode, capping, and insulator component layers. Potential applications and an outlook into future development of metal oxide memristive devices are also outlined.

This paper presents architecture of a contextaware pro-active recommender system. The system uses contextual information in order to provide recommendations that are more suitable to the particular individual user. Reduction-based theory has been used in order to be able to use the contextual information besides the user and item components of traditional two dimensional recommender systems. The proposed recommender system provides recommendations pro-actively by using multi-agent technology. The inference engine of the system uses conditional probability and multiattribute theory in the decision making of what recommendations to be provided to users.