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The analysis of recent large-scale cyber attacks, which leveraged insecure Internet of Things (IoT) devices to perform malicious activities on the Internet, highlighted the rise of IoT-tailored malware/botnets. These malware propagate by scanning the Internet for vulnerable, exploitable IoT devices that could be utilized for further malicious activities. In this paper, we devise a multi-level methodology to investigate Internet-scale reconnaissance activities generated by infected IoT devices. We leverage the Shodan IoT search engine and over 6TB of passive network traffic from a large network telescope (darknet) to infer compromised IoT devices and characterize the generated scanning campaigns. The results highlight a distinctive characteristic of IoT malware/botnets, represented by the targeted ports/services over the analysis interval. Furthermore, while these ports/services are mainly associated with well-known IoT malware/botnets (e.g., Mirai and Satori), we uncovered newly targeted ports, which indicate emerging IoT malware/botnet. Finally, by comparing two instances of analyzed IoT-generated scanning campaigns, we highlight the persistence and evolution of IoT malware/botnets (e.g., ADB.Miner and Fbot), which exploit existing, and in some cases, possibly new vulnerabilities.

An adequate charging infrastructure advocates to ameliorate the range anxiety to propel the disparaged EV market. But, the high installation cost, requirement of suitable places and anticipated immense load on the grid during peak times hinder to elongate the charging station network. Fortunately, the bidirectional energy transferring capability between vehicles may act as an auxiliary solution to charge an EV at any place and at any time without leaning on a stationary charging infrastructure. In this work, we assume a market where charging providers each has a number of charging trucks equipped with a larger battery and a fast charger to charge a number of EVs at some particular parking lots. A provider intends to maximize the served EVs using its limited charging trucksAll charging requests are assumed to be received by an agent which provisions a route and schedule for each charging truck and all trucks should return to the depot after serving EVs. We formulate this combinatorial problem as an ILP to maximize the served EVs . We present a solution methodology by decomposing the problem using Dantzig-Wolfe decomposition approach; we divide the problem into one master and a set of pricing problems and achieve the solution iteratively.

Modern 5G services with stringent reliability and latency requirements such as smart healthcare and industrial automation have become possible through the advancement of Multi-access Edge Computing (MEC). However, the rigidity of ground MEC and its susceptibility to infrastructure failure would prevent satisfying the resiliency and strict requirements of those services. Unmanned Aerial Vehicles (UAVs) have been proposed for providing flexible edge computing capability through UAV-mounted cloudlets, harnessing their advantages such as mobility, low-cost, and line-of-sight communication. However, UAV-mounted cloudlets may have failure rates that would impact mission-critical applications, necessitating a novel study for the provisioned reliability considering UAV node reliability and task redundancy. In this paper, we investigate the novel problem of UAV-aided ultra-reliable low-latency computation offloading which would enable future IoT services with strict requirements. We aim at maximizing the rate of served requests, by optimizing the UAVs’ positions, the offloading decisions, and the allocated resources while respecting the stringent latency and reliability requirements. To do so, the problem is divided into two phases, the first being a planning problem to optimize the placement of UAVs and the second an operational problem to make optimized offloading and resource allocation decisions with constrained UAVs’ energy. We formulate both problems associated with each phase as non-convex mixed-integer programs, and due to their non-convexity, we propose a two-stage approximate algorithm where the two problems are transformed into approximate convex programs. Further, we approach the problem considering the task partitioning model which will be prevalent in 5G networks. Through numerical analysis, we demonstrate the efficiency of our solution considering various scenarios, and compare it to other baseline approaches.

This special section features the latest research contributions regarding the design and management of reliable networks. Reliability of communication infrastructure is a top priority for network operators. To ensure reliable network operation, new design and management techniques for reliable communications must be constantly devised to respond to the rapid network and service evolution. As a recent and relevant example, deployments of 5G communication networks will soon enter their second phase, during which the network infrastructure will require upgrades to support new Ultra-Reliable Low-Latency Communication (URLLC) services with availabilities of up to 6 nines to be guaranteed jointly with extremely low latencies. Even in the still preliminary vision of 6G communication networks, reliability is posed as one of the most critical requirements, as 6G networks will represent the communication platform of our future hyper-connected society, supporting essential services as smart mobility, e-health, and immersive environments with application in remote education and working, just to name a few. Similarly, disaster resiliency in communication networks is now attracting the attention of media, government and industry as never before (consider, e.g., the worldwide network traffic deluge to support remote working during the current Coronavirus pandemic). Luckily, several new technical directions can be leveraged to provide new solutions for network reliability as: increased network reconfigurability enabled by Software Defined Networking (SDN); integration/convergence of multiple technologies (optical, wireless satellite, datacenter networks); enhanced forms of data/service replication, supported by, e.g., edge computing; network slicing, used to carve highly-reliable logical partitions of network, computing and storage resources. These, and many others, technological transformations can be leveraged to enable next-generation high-reliability networks.

The work presented in this article aims at improving the ground vehicle connectivity in the context of an intermittent vehicle-to-UAV (V2U) communication scenario where vehicles opportunistically establish time-limited connectivity with passing by unmanned aerial vehicles (UAVs) serving as flying base stations responsible for routing incoming vehicle data over backbone networks and/or the Internet. As opposed to existing work in the literature where vehicles are only allowed to establish direct connectivity with in-range UAVs, this work aims at also exploiting the possible formation of vehicular clusters and, hence, the feasibility of intervehicular communications to establish multihop paths connecting source vehicles to destination UAVs. A mathematical model is presented for the purpose of capturing the nodal (i.e., vehicles and UAVs) mobility dynamics and derive an expression for the overall V2U connectivity probability as well as the overall average vehicle connection time. Extensive simulations are conducted in order to adduce the validity and accuracy of the proposed model and provide further insights into the connectivity sensibility to fundamental system parameters.

The fifth generation (5G) networks are expected to support diverse business verticals (i.e., manufacturing, health care, etc.) with varying quality of service requirements. While today’s mobile networks are a one size fits all architecture, tomorrow’s 5G mobile networks are envisioned to encourage agility, programmability and elasticity through enabling a software-based architecture promoted by network slicing. Network slicing is a new paradigm consisting of partitioning the underlying network infrastructure into different logical network slices, each dedicated to address the requirements (i.e., ultra-low latency, ultra-reliability, etc.) of a group of services. Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies have been identified as main enablers of network slicing, facilitating the fulfillment of the aforementioned services’ requirements. In this paper, we study the Latency-Aware service scheduling (LASS) problem to solve the network function mapping, the traffic routing and the network service scheduling in the context of an ultra-low latency network slice to consider services with stringent deadlines. We propose the LASS-Game, a novel game-theoretic approach presenting a scalable solution for the LASS problem that accounts for the centralized aspect of the problem while leveraging a decentralized mapping, routing and scheduling decisions.

The emergence of Internet of Things malware, which leverages exploited IoT devices to perform large-scale cyber attacks (e.g., Mirai botnet), is considered as a major threat to the Internet ecosystem. To mitigate such threat, there is an utmost need for effective IoT malware classification and family attribution, which provide essential steps towards initiating attack mitigation/prevention countermeasures. In this paper, motivated by the lack of sophisticated malware obfuscation in the implementation of IoT malware, we utilize features extracted from strings- and image-based representations of the executable binaries to propose a novel multi-dimensional classification approach using Deep Learning (DL) architectures. To this end, we analyze more than 70,000 recently detected IoT malware samples. Our in-depth experiments with four prominent IoT malware families highlight the significant accuracy of the approach (99.78%), which outperforms conventional single-level classifiers. Additionally, we utilize our IoT-tailored approach for labeling newly detected “unknown” malware samples, which were mainly attributed to a few predominant families. Finally, this work contributes to the security of future networks (e.g., 5G) through the implementation of effective tools/techniques for timely IoT malware classification, and attack mitigation.

Disaster management, search and rescue missions, and health monitoring are examples of critical applications that require object localization with high precision and sometimes in a timely manner. In the absence of the global positioning system (GPS), the radio received signal strength index (RSSI) can be used for localization purposes due to its simplicity and cost-effectiveness. However, due to the low accuracy of RSSI, unmanned aerial vehicles (UAVs) or drones may be used as an efficient solution for improved localization accuracy due to their agility and higher probability of line-of-sight (LoS). Hence, in this context, we propose a novel framework based on reinforcement learning (RL) to enable a UAV (agent) to autonomously find its trajectory that results in improving the localization accuracy of multiple objects in shortest time and path length, fewer signal-strength measurements (waypoints), and/or lower UAV energy consumption. In particular, we first control the agent through initial scan trajectory on the whole region to 1) know the number of nodes and estimate their initial locations, and 2) train the agent online during operation. Then, the agent forms its trajectory by using RL to choose the next waypoints in order to minimize the average location errors of all objects. Our framework includes detailed UAV to ground channel characteristics with an empirical path loss and log-normal shadowing model, and also with an elaborate energy consumption model. We investigate and compare the localization precision of our approach with existing methods from the literature by varying the UAV's trajectory length, energy, number of waypoints, and time. Furthermore, we study the impact of the UAV's velocity, altitude, hovering time, communication range, number of maximum RSSI measurements, and number of objects. The results show the superiority of our method over the state-of-art and demonstrates its fast reduction of the localization error.

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile users. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For mobile users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and the system performance.

In this letter, we investigate the amalgamation between the reconfigurable intelligent surface (RIS) technology and the joint transmission coordinated multipoint (JT-CoMP) in order to enhance the performance of a cell-edge user equipment (UE) in a two-user non-orthogonal multiple access (NOMA) group without deteriorating the performance of the NOMA cell-center UE. The RIS is adopted to construct a strong combined channel gain at the cell-edge UE, while JT-CoMP is used to mitigate the effects of inter-cell interference (ICI). In this proposed framework, we derive first a closed-form expression for the ergodic rate of the cell-edge UE, and then we evaluate the network spectral efficiency. We validate the derived expression through Monte-Carlo simulations, where we demonstrate the efficacy of the proposed framework compared to other multiple access techniques proposed in the literature.

In this paper, we investigate the integration between the coordinated multipoint (CoMP) transmission and the non-orthogonal multiple access (NOMA) in downlink heterogeneous cloud radio access networks (H-CRANs). In H-CRAN, low-power high-density small remote radio heads (SRRHs) are underlaid by high-power low-density macro RRH (MRRH). However, co-channel deployment of the different RRHs gives rise to the problem of inter-cell interference that significantly affects system performance especially the cell-edge users. Thus, the users are first categorized into Non-CoMP users and CoMP users based on the relation between the useful signal to the dominant interference signal. The Non-CoMP user is the user equipment (UEs) having high signal-to-interference-plus-noise-ratio ( $\mathtt {SINR}$ ) and hence associates with only one RRH. On the other hand, the CoMP user, cell-edge user, is the UE that experiences less distinctive received power with the best two RRHs. In the proposed CoMP-NOMA framework, each RRH schedules CoMP-UE and non-CoMP-UE over the same transmission channel using NOMA. We first design an analytical framework based on tools from the stochastic geometry to evaluate the performance of the proposed framework (CoMP-NOMA) which is based on H-CRAN in terms of the average achievable data rate for each NOMA UE. We then examine the spectral efficiency of the proposed CoMP-NOMA based H-CRAN. Simulation results are provided to validate the accuracy of the analytical models and to reveal the superiority of the proposed CoMP-NOMA framework compared with conventional CoMP orthogonal multiple access (CoMP-OMA) techniques. By reaping the benefits of both JT-CoMP and NOMA, we prove that the proposed framework can successfully deal with the inter-cell interference by using CoMP and improve the network’s spectral efficiency through NOMA technique. We also show that, with an appropriate power allocation coefficient setting at the Non-CoMP-UEs, a fairness performance can be achieved between the CoMP-UEs and the Non-CoMP-UEs.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs’ trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

Analyzing the characteristics of scanning activities generated by compromised Internet-of-Things (IoT) devices is instrumental for early detection of IoT malware propagation. In this letter, we leverage about 3 TB of empirical passive network measurements to investigate IoT-generated scanning activities. Specifically, we exploit stochastic processes to model low-rate scans by incorporating the effect of random sampling and jitter on the observed packet Inter-Arrival Times (IAT). We verify the derived formulations using simulated results and empirically explore scans targeting common services (Telnet and HTTP) to demonstrate the effectiveness of our approach towards modeling low-rate scans while generating practical cyber threat intelligence.

The transition to electric vehicles (EVs) has prodigious plausibility in reducing green house gas (GHG). But EVs acceptance is, however, hindered by several challenges; among them is their avidity for quicker charging at lower price. This article considers a photovoltaic (PV)-powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate-dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainty. An integer linear programming (ILP)-based centralized system is first proposed to minimize the charging price per EV. Due to the larger computational time, we subsequently present two game theoretic algorithms, i.e., game 1 and game 2. In game 1, players are oblivious of upcoming charging requests, whereas in game 2, players consider the future anticipated load to select their charging strategies. The games are shown to converge to a Nash equilibrium. The average unit price of game 2 is shown to be the same as the one of the optimal solution and takes considerably less computation time than the centralized method.

The inter-cell interference (ICI) and the intra-user interference in non-orthogonal multiple access (NOMA) cellular networks have serious impacts on the performance of cell edge users. In this letter, we investigate the integration of coordinated multipoint (CoMP) transmission and cooperative NOMA (C-NOMA) aiming to improve the performance of cell edge users. Using this framework, we exploit the cooperation between base stations (BSs) to mitigate the ICI and the successive decoding of users that are near the BSs to further enhance the performance of cell edge users. In this setting, we derive a closed form expression for the outage probability of a cell edge user along with an analytical expression for its ergodic rate. We validate the derived expressions through various Monte-Carlo simulations, where we show the superiority of the proposed framework compared with other multiple access schemes proposed in the literature.

The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.

The Vehicle-as-a-Resource is an emerging concept that allows the exploitation of the vehicles’ computational resources for the purpose of executing tasks offloaded by passengers, vehicles, or even an Internet-of-Things devices. This article revolves around a scenario where a roadside unit located at the edge of a hierarchical multitier edge computing subnetwork resorts to the utilization of idle vehicles computational resources through a fog-enabled substructure yielding a cost-effective computational task offloading solution. In this context, scheduling the offload of these tasks to the appropriate vehicles is a challenging problem that is subject to the interaction of major role-playing parameters. Among these parameters are the variability of vehicles availability and their computational power, the individual tasks’ weighted priorities and their deadlines, the tasks required computational power as well as the required data to upload/download. This article proposes an infrastructure-assisted task scheduling scheme where the roadside unit receives computational tasks from different sources and schedules these tasks over a computationally capable vehicle residing within the roadside unit’s range. The aim is to maximize the weighted number of admitted tasks while considering the constraints mentioned above. Compared to other works, this article broaches a more realistic scenario by considering a more accurate computational task and system model. Our system considers both the latency and throughput of task accomplishments by maximizing the weighted number of admitted tasks while at the same time respecting the tasks accompanied deadlines. Both radio and computational resources are part of the optimization problem. After proving the NP-hardness of the scheduling problem, we formulated the problem as a mixed-integer linear program. A Dantzig–Wolfe decomposition algorithm is proposed which yields to a master program solvable by the Barrier algorithm and subproblems solved optimally with a polynomial-time dynamic programming approach. Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of our approach compared to branch and bound and greedy algorithms.

The wireless industry is moving towards developing smart cellular architectures that dynamically adjust the use of the network elements according to the service demand, and automating their operations in order to minimize both capital expenditure (CAPEX) and operation expenditure (OPEX). This involves developing efficient and unsupervised radio access network (RAN) planning, which has a direct impact on the system performance and CAPEX. This intelligent cellular planning aims at providing the base stations (BSs) configurations (e.g., coverage, user associations and antenna radiation pattern) that minimize the number of deployed BSs and meet the requirements in terms of coverage and capacity. The cellular planning optimization problem has been shown to be complex and non-scalable. Moreover, most of the existing cellular planning techniques result in an over or under provisioning architecture. Motivated by the above, we propose in this paper a novel and efficient unsupervised planning process. We make use of statistical machine learning (SML) to solve the problem at hand. The core idea of SML is that the planning parameters are treated as random variables. The parameters that maximize the corresponding joint probability distribution, conditioned on observations of users’ positions, are learned or inferred using Gibbs sampling theory and Bayes’ theory. To apply this theory to the planning problem, we make significant efforts to properly formulate the problem to be able to incorporate the constraints into the inference process and extract the planning parameters from the inferred model. Through several numerical examples, we compare the performance of the proposed approach to clustering-based and optimization-based existing planning approaches, and demonstrate the efficacy of our approach. We also demonstrate how our approach can leverage existing cellular infrastructures into the new design.

This paper investigates the data-delivery latency in the context of intermittent vehicle-to-UAV (V2U) communications. Precisely, a V2U communication scenario is considered where vehicles opportunistically establish connectivity with passing by UAVs for a limited period of time during which these vehicles transmit data packets to in-range UAVs serving as flying base stations and, in turn, are responsible for delivering these packets to backbone networks and/or routing them over the Internet. A mathematical framework is established with the objective of modeling the vehicles’ OnBoard Units’ (OBUs’) buffers as single-server queueing systems. The established queueing model will allow for the evaluation of the V2U communication system in terms of the average data packet delivery delay. Extensive simulations are conducted with the objective of asserting the validity and accuracy of the proposed queueing model as well as providing further insights into the delay sensibility to various system parameters.

Undoubtedly, these days our telecommunication networks are witnessing not only a major spike is data volumes, but also a shift in the mode of communications. Employees, news anchor and students are conducting their daily business and learning activities through online platforms as they shelter homes during this pandemic and this is expected to continue for some time. An overwhelming shift to one-to-many and many-to-many communications is observed and end users expect from their providers efficient, secure and reliable services. Operators of digital platforms are challenged to respond quickly to the rising demand, by enhancing deployability and manageability of their service. Virtualization is a key enabler for enhanced deployability and manageability where virtual functions can be automatically deployed on demand. Another challenge that providers deal with is the individualized requirements by services offered to users which may vary between high reliabilities, low latency, robust security and any combination thereof. This paper considers the problem of provisioning multi-source multicast services where each service consists of a set of in-network virtual functions that must be chained in a particular order to meet the quality of service demanded by end users. We deal with a reliable service where reliability is attained by provisioning backup functions for the service. We first calculate the requirements of VNF backups which account for fewer computing resource consumption. Next, we formulate the multi-source multicast hybrid routing as a Mixed Integer Linear Programming (MILP) and find a solution with optimal VNF placement and traffic routing. We also proposed a K-shortest path-based greedy algorithm to reduce the complexity for solving MILP. Numerical analysis and simulations are conducted to validate the proposed algorithms. Our results show multi-source multicast has a better routing selection compared to single-source multicast due to the more options of multicast sources for providing a reliable network service.

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV’s flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.

The precision time protocol (PTP) is considered as one of the most favorable mechanisms for providing unified and precise time at the substation level in the smart grid. Nevertheless, PTP was shown to be vulnerable to cyber-attacks targeting its components and synchronization services. In this article, we capitalize on the theory and outcome of our previous work to contribute a more complete solution that addresses PTP cyber security. We propose to close the PTP loop through an extension that introduces new functionality and messages. This extension covers the PTP attack surface and enables the detection of attacks on PTP time synchronization. We formally model and verify the proposed extension using UPPAAL model checker. In addition, we validate the proposed extension using Omnet++ simulation. The evaluation demonstrates that our approach preserves PTP functionality, while successfully detecting cyber attacks against PTP components in a timely manner.

Heterogeneous networks have allowed network operators to enhance the spectral efficiency and support large number of devices by deploying close small-cells. Recently, Multi-access Edge Computing (MEC) has become an enabler for modern latency-sensitive 5G services by pushing tasks computation to the network edge. In this paper, we study the problem of task offloading in a MEC-enabled heterogeneous network with low-cost wireless backhaul, where we minimize the total devices’ energy consumption while respecting their latency deadline. We explore the benefit of leveraging the macro-cell cloudlet for computing small-cell users’ tasks, where the allocation of backhaul radio resources is optimized. We also jointly optimize the partial offloading decision, transmit power, and the allocation of access radio and computational resources. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method that provides an approximate solution. Through numerical analysis, we perform simulations based on varying configurations, and demonstrate the performance and efficiency of our proposed solution.

This paper addresses the problems of data sanitization and cyber situational awareness by analyzing 910 GB of real Internet-scale traffic, which has been passively collected by monitoring close to 16.5 million darknet IP addresses from a /8 and a /13 network telescopes. First, the paper offers a novel probabilistic darknet preprocessing model, which aims at sanitizing darknet data to prepare it for effective use in the task of cyber threat intelligence generation. Such model has been engineered using a distributed multithreaded approach, rendering it operational and highly effective on darknet big data. Second, the paper further contributes by presenting an innovative approach to infer large-scale orchestrated probing campaigns by leveraging darknet data, for Internet cyber situational awareness. The approach uniquely reduces the dimensionality of such big data by utilizing its artifacts, instead of processing the actual raw data. This is accomplished by extracting and analyzing probing time series using formal methods rooted in Fourier transform and Kalman filtering. Thorough empirical evaluations indeed validate the accuracy and the performance of the proposed methods and techniques. We assert that the darknet sanitization model and the probing orchestration inference approach are of significant value, given their postulated highly applicable nature to the field of Internet measurements for cyber security in the era of big data.

Along with the dramatic increase in the number of IoT devices, different IoT services with heterogeneous QoS requirements are evolving with the aim of making the current society smarter and more connected. In order to deliver such services to the end users, the network infrastructure has to accommodate the tremendous workload generated by the smart devices and their heterogeneous and stringent latency and reliability requirements. This would only be possible with the emergence of ultra reliable low latency communications (uRLLC) promised by 5G. Mobile Edge Computing (MEC) has emerged as an enabling technology to help with the realization of such services by bringing the remote computing and storage capabilities of the cloud closer to the users. However, integrating uRLLC with MEC would require the network operator to efficiently map the generated workloads to MEC nodes along with resolving the trade-off between the latency and reliability requirements. Thus, we study in this paper the problem of Workload Assignment (WA) and formulate it as a Mixed Integer Program (MIP) to decide on the assignment of the workloads to the available MEC nodes. Due to the complexity of the WA problem, we decompose the problem into two subproblems; Reliability Aware Candidate Selection (RACS) and Latency Aware Workload Assignment (LAWA-MIP). We evaluate the performance of the decomposition approach and propose a more scalable approach; Tabu meta-heuristic (WA-Tabu). Through extensive numerical evaluation, we analyze the performance and show the efficiency of our proposed approach under different system parameters.

Middleboxes have gained popularity due to the significant value-added services these network elements provide to traffic flows, in terms of enhanced performance and security. Policy-aware traffic flows usually need to traverse multiple middleboxes in a predefined order to satisfy their associated policy, also known as Service Function Chaining . Typically, Middleboxes run on specialized hardware, which make them highly inflexible to handle the unpredictable and fluctuating-nature of traffic, and contribute to significant capital and operational expenditures (Cap-ex and Op-ex) to provision, accommodate, and maintain them. Network Function Virtualization is a promising technology with the potential to tackle the aforementioned limitations of hardware middleboxes. Yet, NFV is still in its infancy, and there exists several technical challenges that need to be addressed, among which, the Virtual Network Function assignment problem tops the list. The VNF assignment problem stems from the newly gained flexibility in instantiating VNFs (on-demand) anywhere in the network. Subsequently, network providers must decide on the optimal placement of VNF instances which maximizes the number of admitted policy-aware traffic flows across their network. Existing work consists of Integer Linear Program (ILP) models, which are fairly unscalable, or heuristic-based approaches with no guarantee on the quality of the obtained solutions. This work proposes a novel Logic-Based Benders Decomposition (LBBD) based approach to solve the VNF assignment problem. It consists of decomposing the problem into two subproblems: a master and a subproblem; and at every iteration constructive Benders cuts are introduced to the master to tighten its search space. We compared the LBBD approach against the ILP and a heuristic method, and we show that our approach achieves the optimal solution (as opposed to heuristic-based methods) 700 times faster than the ILP.

Vehicular networks’ connectivity and data delivery delay performance is highly affected by the vehicular traffic's spatio-temporal dynamics whose variations are subject to a multitude of random factors. Under the stringent and inevitable limitations imposed by free-flow vehicular traffic conditions (i.e., low-to-medium vehicular densities, elevated degree of mobility, high speeds, etc), these networks suffer from considerably rapid topology variations leading to severe connectivity intermittence and, hence, delayed data delivery. This motivates the study presented in this paper, which aims at investigating the capability of external elements that are independent of the vehicular traffic flow and its inherent limitations (e.g., airborne unmanned aerial vehicles (UAVs), a.k.a., drones) to serve as possible adjuvant relays; thus, contributing to strengthening/healing weak/broken communication links among ground-bound vehicular entities (i.e., RoadSide Units (RSUs) and vehicles) and uplifting the vehicular connectivity and delay performance. Particularly, in the context of a vehicular sub-networking scenario, a UAV mobility model is proposed as a first step in analytically capturing macroscopic dynamics for UAVs exhibiting waypoint mobility patterns and plying over a considered roadway segment. Then, a stochastic analytical model is formulated for the purpose of mathematically characterizing the path availability and achieved data delivery delays in the presence of these UAVs. A simulation framework is established to verify the validity and accuracy of the proposed models and gauge the merit of UAV assistance in improving the vehicular connectivity and data delivery delay performance.

In order to support the development of 5G technologies, researchers are actively engaged in addressing the challenges accompanying the emerging 5G applications. Unquestionably, an eminent technology gaining significant research attention is edge computing. Vehicular edge computing brings data storage and computing capabilities as well as hosting support applications that comprise emerging vehicular services and applications which demand low-delay processing, to the edge closer to the vehicles, reducing response time and increasing reliability, therefore achieving the holistic vision of the tactile Internet. In this context, this paper considers a vehicular network with edge computing capabilities deployed at road side units, and addresses the problem of workload offloading as well as scheduling of computation tasks on the computing resources available at the edge. The challenge here is the high mobility of the vehicles and hence their short residence time within the coverage range of the road side units hosting the edge computing resources. A joint problem considering the communication and computation resources, as well as the latency requirements of the workload is formulated and the scheduling is shown to be NP-Hard. Subsequently, efficient solutions based on Lagrangian relaxation are derived and presented. We evaluate numerically the proposed methods and show their closeness to the optimal solutions.

Service restoration (SR) through network configuration in power systems is a highly investigated problem. In SR, utilities reconfigure the transmission and distribution networks to supply the consumer load demands. However, the advancements in distributed energy storage (DES) systems at the consumer side define a new dimension for SR. In this paper, we approach the network reconfiguration for SR in the presence of DES through mathematical modeling. We present a mathematical model that captures the properties of the power system, and reconfigures the network to supply consumer demand over available lines. This model considers power supply from DES, and proposes the least cost SR plan. We evaluate the proposed approach on the IEEE 14-, 30-, and 57-Bus systems, and report on the collected results. The collected results demonstrate the importance of the available DES in power SR.

The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, multi-access edge computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators, requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC resource provisioning and workload assignment for IoT services (RPWA) as a mixed integer program to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the delay aware load assignment sub-problem and the mobile edge servers dimensioning sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters.

Multi-access edge computing (MEC) has formed a major improvement in the existing mobile cloud computing paradigm, due to its ability in addressing the rising number of latency-sensitive services. However, bearing in mind the limited capacity that edge servers possess which offsets their benefits in the periods of high load, a hierarchical arrangement of the edge cloudlets has been studied and has shown to be successful in expanding their capabilities. Yet, considering the emerging business models in 5G networks, the cost disparity between the edge tiers has been until now ignored, leading to cost-inefficient solutions with respect to the network operators (NOs). In this paper, we consider an NO that is leasing resources of a high-tier central cloudlet for task offload, where we jointly minimize the NO’s computational cost and devices’ energy consumption in a multi-tier MEC system, by optimizing the offloading decision, the allocated transmission power and radio resources on the uplink channel, and the assigned servers’ computation, while respecting the devices’ latency requirement. We mathematically formulate our mixed-integer non-convex program and propose a Branch-and-Bound (BnB) algorithm for obtaining the optimal solution. Due to the BnB complexity, we propose a low-complexity algorithm based on the successive convex approximation method to solve and obtain a high-quality solution and also present an inflation-based algorithm for obtaining a polynomial-time and efficient solution. The numerical results show the performance and scalability of the algorithms, demonstrate their efficiency, and uncover insights for helping NOs to better manage their resources following various configurations.

Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. In an effort to satisfy the driver’s well-being and demand for continuous connectivity in the IoV era, this paper addresses both safety and quality-of-service (QoS) concerns in a green, balanced, connected, and efficient vehicular network. Using the recent advances in training deep neural networks, we exploit the deep reinforcement learning model, namely deep Q-network, which learns a scheduling policy from high-dimensional inputs corresponding to the current characteristics of the underlying model. The realized policy serves to extend the lifetime of the battery-powered vehicular network while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform several scheduling benchmarks in terms of completed request percentage (10–25%), mean request delay (10–15%), and total network lifetime (5–65%).

Hardware middleboxes are critical elements in today’s networks. Despite their important roles, they are accompanied by several problems, namely, their lack of flexibility, high capital and operational expenditures, and power consumption. Owing to the recent advances in virtualization, network function virtualization promises to address these problems, through replacing hardware middleboxes by software-based entities which can run on commodity hardware. These virtual network functions (VNFs) promise to alleviate the numerous disadvantages brought by their hardware counterparts. One of these most serious issues is the steadily increasing power consumption. In order to further optimize the power consumption, an efficient framework capable of mapping and scheduling traffic on these VNFs is needed. Such a framework allows to optimally assign and schedule the flows to be serviced, and place the unused servers in energy saving modes. In this paper, we assume VNFs are already placed on physical machines and consider traffic flows with deadlines. We focus on the problem of assigning and scheduling flows to VNFs in the most energy efficient manner. We formulate this problem mathematically and, owing to its complexity, present an efficient algorithmic method for solving it. We compare our heuristic with two other approaches, one of which aims to minimize the makespan and the other to minimize number of servers used. We show that our heuristic combines the advantages of both approaches and generates better results by consuming up to 31.3% and 46.1% less energy than other two approaches.

Time distribution mechanisms favored for use in the smart grid, such as precision time protocol (PTP), were not designed with security in mind, and thus suffer several security vulnerabilities. PTP is vulnerable to fake timestamp attacks through master impersonation and sync message injection. Such an attack will synchronize clocks to a false time reference. In this letter, we consider an IEC 61850 substation and propose an approach to detect fake timestamps communicated through false PTP sync messages. This approach builds on top of existing network and system management solutions. We introduce a new simple network management protocol data objects to monitor PTP functionality and detect the existence of fake timestamps in a PTP synchronized network. The approach is implemented on a testbed. The collected results demonstrate its ability to protect against fake timestamp attacks.

The increasing need for reducing greenhouse gas emissions and the drive for green cities have promoted the use of electric vehicles due to their environmental benefits. In fact, countries have set their own targets and are offering incentives for people to purchase EVs as opposed to traditional gasoline-powered cars. Manufacturers have been hastily deploying charging stations to meet the charging requirements of the EVs on the road. This rapid deployment has contributed to the EV ecosystem's lack of proper security measures, raising multiple questions related to the power grid security and vulnerability. In this paper, we offer a complete examination of the EV ecosystem from the vulnerability to the attacks and finally the solutions. We start by examining the existing vulnerabilities in the EV ecosystem that can be exploited to control the EV charging and launch attacks against the power grid. We then discuss the non-linear nature of the EV charging load and simulate multiple attacks that can be launched against the power grid using these EVs. EV loads have high reactive power demand which can have a larger impact on the grid compared to residential loads. We perform simulations on two power grids and demonstrate that while the grid can recover after a 48 MW attack utilizing traditional residential loads, a smaller 30 MW EV load attack can completely destabilize the system. Finally, we suggest several patches for the existing vulnerabilities and discuss two methods aimed at detecting EV attacks., Comment: International Journal of Electrical Power & Energy Systems, Available online 21 November 2021, 107784, Full citation details will be added when the final version becomes available

Application-layer protocols are widely adopted for signaling in telecommunication networks such as the 5G networks. However, they can be subject to application-layer attacks that are hardly detected by existing traditional network-based security tools that often do not support telecommunication-specific applications. To address this issue, we propose in this work AutoGuard, a proactive anomaly detection solution that employs application-layer Performance Measurement (PM) counters to train two different Deep Learning (DL) techniques, namely, Long Short Term Memory (LSTM) networks and AutoEncoders (AEs). We leverage recent advancements in Machine Learning (ML) that show the advantages brought by combining multiple ML models to build a dual-intelligence approach allowing the proactive detection of application layer anomalies. Our proposed dual-intelligence solution promotes signaling workload forecasting and anomaly prediction as a proactive security control in 5G networks. As a proof of concept, we implement our approach for the proactive detection of Diameter-related signaling attacks on the Home Subscriber Server (HSS) core network function. To evaluate our solution, we conduct a set of experiments using data collected from a real 5G testbed. Our results show the effectiveness of our dual intelligence approach on proactively detecting signaling anomalies with a precision reaching 0.86.

Intelligent Transportation Systems (ITS) are gaining substantial attention owing to the great benefits offered to the vehicle users. In ITS paradigm, content data is normally obtained from road side units (RSUs). However, in some scenarios, terrestrial networks are partially/temporarily out-of-service. Unmanned Aerial Vehicle (UAV) or drone cells are expected to be one of the pillars of future networks to assist the vehicular networks in such scenarios. To this end, we propose a collaborative framework between UAVs and in-service RSUs to partial service vehicles. Our objective is to maximize the amount of downloaded contents to vehicles while considering the dynamic nature of the network. Motivated by the success of machine learning (ML) techniques particularly deep Reinforcement learning in solving complex problems, we formulate the scheduling and content management policy problem as a Markov Decision Process (MDP) where the system state space considers the vehicular network dynamics. Proximal Policy Optimization (PPO) is utilized to govern the content decisions in the vehicular network. The simulation-based results show that during the mission time, the proposed algorithm learns the vehicular environment and its dynamics to handle the complex action space.

Ameliorating the range anxiety to propel the disparaged electric vehicle (EV) market necessitates an adequate charging infrastructure. But, the high initial installation cost, requirement of suitable places and the anticipated immense load on the grid during peak time hinder to elongate the charging station network, especially in urban areas. As a consequence, the bidirectional energy transferring capability between vehicle to vehicle (V2V) may act as an auxiliary solution to charge an EV at any place and at any time without leaning on a permanent charging infrastructure. Here in this work, we assume a company having a number of V2V enabled charging trucks equipped with a larger battery and a fast charger to charge a number of EVs at some particular parking lots. The company intends to maximize the served number EVs, when an EV should be considered as served if it would be fully charged during its declared charging window. All the charging requests are assumed to be received before the time horizon and we also consider that all trucks should return to the depot after serving EVs. We formulate an integer linear program (ILP) to maximize the number of served EVs by determining the optimal trajectory of each truck. The problem is formally proved as NP-hard and due to its larger computational time, we also propose three different heuristic algorithms: 1) Strictest Window Shortest Path First (SWSPF), 2) Smallest Demand Shortest Path First (SDSPF) and 3) Earliest Arrival Shortest Path First (EASPF). The performance of these three algorithms are examined in detail and finally, SDSPF shows the better performance and its performance is closer to the optimal solution.

Network Function Virtualization (NFV) is a transformation of traditional proprietary network designs to a more agile and software based environment. NFV architecture is considered as a key enabler for 5G as it offers the flexible deployment, reduced setup costs and less-time-to-market for the new services. Current studies on NFV in unicast transmission case can not be extended to multicast. Owing to the recent popularity and growing interest for live video streaming applications, efficient multicast solutions in NFV-enabled networks are needed. In this paper, we propose an NFV multicast resource optimization model as a Mixed Integer Linear Program (MILP) exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements along with two heuristics algorithms. We evaluate the performance of the proposed algorithms on different network topologies. Simulation results prove that the proposed algorithms outperform the existing solution in terms of reduced bandwidth consumption and the delay values.

Due to the dramatic increase in high data rate services and in order to meet the demands of the fifth-generation (5G) networks, researchers from both academia and industry are exploring advanced transmission techniques, new network architectures and new frequency spectrum such as the visible light and the millimeter wave (mmWave) spectra. Visible light communication (VLC) particularly is an emerging technology that has been introduced as a promising solution for 5G and beyond, owing to the large unexploited spectrum, which translates to significantly high data rates. Although VLC systems are more immune against interference and less susceptible to security vulnerabilities since light does not penetrate through walls, security issues arise naturally in VLC channels due to their open and broadcasting nature, compared to fiber-optic systems. In addition, since VLC is considered to be an enabling technology for 5G, and security is one of the 5G fundamental requirements, security issues should be carefully addressed and resolved in the VLC context. On the other hand, due to the success of physical layer security (PLS) in improving the security of radio-frequency (RF) wireless networks, extending such PLS techniques to VLC systems has been of great interest. Only two survey papers on security in VLC have been published in the literature. However, a comparative and unified survey on PLS for VLC from information theoretic and signal processing point of views is still missing. This paper covers almost all aspects of PLS for VLC, including different channel models, input distributions, network configurations, precoding/signaling strategies, and secrecy capacity and information rates. Furthermore, we propose a number of timely and open research directions for PLS-VLC systems, including the application of measurement-based indoor and outdoor channel models, incorporating user mobility and device orientation into the channel model, and combining VLC and RF systems to realize the potential of such technologies.

We study, in this paper, the secrecy performance of a multi-user (MU) multiple-input single-output visible light communication broadcast channel with confidential messages. The underlying system model comprises $K +1$ nodes: a transmitter (Alice) equipped with $N$ fixtures of LEDs and $K$ spatially dispersed users, each equipped with a single photo-diode. The MU channel is modeled as deterministic and real-valued and assumed to be perfectly known to Alice, since all users are assumed to be active. We consider typical secrecy performance measures, namely, the max–min fairness, the harmonic mean, the proportional fairness, and the weighted fairness. For each performance measure, we derive an achievable secrecy rate for the system as a function of the precoding matrix. As such, we propose algorithms that yield the best precoding matrix for the derived secrecy rates, where we analyze their convergence and computational complexity. In contrast, what has been considered in the literature so far is zero-forcing (ZF) precoding, which is suboptimal. We present several numerical examples through which we demonstrate the substantial improvements in the secrecy performance achieved by the proposed techniques compared with those achieved by the conventional ZF. However, this comes at a slight increase in the complexity of the proposed techniques compared with ZF.

In this paper, we investigate the downlink transmissions in wireless backhaul (WB) networks when unmanned aerial vehicles (UAVs) are used as flying small cell base stations. We propose to employ the non-orthogonal multiple access (NOMA) on the WB transmissions and introduce a novel cooperative transmission scheme for the wireless access links. Then, we formulate an optimization problem which jointly determines the radio resource allocation at the macro cell base station (MBS) and UAVs along with the optimization of the decoding order of the NOMA process and the positions of the UAVs in space to maximize the sum achievable rate of all users. The formulated problem is a general mixed integer non-convex program, which is very difficult to solve optimally within a polynomial time. Therefore, we propose a framework based on the method of difference of convex program characterized by the Lipschitz continuity to transform and approximate the original problem into a series of convex approximate ones and develop a low-complexity algorithm to sequentially solve for each approximate problem until convergence. Numerical evaluation and analysis show that our achieved solution, under the proposed framework and developed algorithm, can outperform the other schemes which aim at either optimizing without using cooperative NOMA or do not optimize the UAVs' positions.

Today, the evolution of smart grid, electric vehicles (EVs) with voltage to the grid mode, and deployment of renewable energy sources (RESs) are bringing revolutionary changes to the existing electrical grid. Volt-VAR optimization (VVO) is a well-studied problem, for bringing solutions to reduce the losses and demand along the distribution lines. The current VVO, however, does not acknowledge the role of elastic and inelastic loads, EVs, and RESs to reduce the reactive power losses and hence the cost of generation. We propose a mathematical model Volt-VAR and CVR optimization (VVCO)/optimal energy consumption model (OECM) to solve the VVO problem by considering load shifting, EV as the storage and carrier of the energy, and use of RES. The VVCO/OECM not only reduces the reactive load but also flatten the load curve to reduce the uncertainty in the generation and to decrease the cost. The system also considers the efficiency of the electrical equipment to enhance the lifetime of the devices. We develop a non-cooperative game to solve the VVCO/OECM problem. To evaluate the performance, we simulate the VVCO/OECM model and compare with the existing VVO solution. We found that our method took almost a constant time to produce a solution of VVO regardless of the size of the network. The proposed method also outperform the existing VVO solution by reducing the generation cost and flatten the load and minimizes the uncertainty in the power generation. Results have shown that exploiting RES will reduce the voltage drop through reducing the injection of reactive power to the system.

Smart grid applications demand the availability of a reliable and accurate time signal. Measurements and events need to be correctly aligned to enable proper actions and decisions. Precision time protocol (PTP) is the favored protocol for time distribution across smart grid domains. The correct functionality of PTP is of paramount importance and its security is of high priority. To harden its security, detection, and prevention mechanisms against attacks targeting PTP are needed. In this paper, we propose detection and mitigation mechanisms against the known PTP delay attack. We apply model checking to quantify the effect of the delay attack. Moreover, the validity of the proposed mechanism is formally proven. The suggested approach is tested on a physical system. The collected results support the usefulness of the mechanism in detecting the delay attacks targeting PTP, and preserving the system functionality.

In this paper, we propose to re-engineer the wireless backhaul two-tier heterogeneous networks architecture by developing a novel cooperative transmission scheme based on non-orthogonal multiple access (NOMA). To effectively manage severe interference from the newly introduced backhaul communications, we employ the cochannel time division duplexing combined with spectrum partitioning between two considered tiers. This paper’s novelty lies in the formulation to solve for the NOMA decoding order, which affects the rule of the cooperation between small cell transmissions. We propose two optimization problems of jointly designing the NOMA decoding order together with the transmit beamforming at the macro base station and power allocation at the small cells which maximize the total achievable rate and the number of satisfied users, respectively. The first and second formulated problems are both mixed integer non-convex and are generally NP-hard. To solve them, we first employ the difference of convex functions to present the formulated binary variables and then equivalently transform the optimization problems into more tractable forms. Finally, we develop an iterative low-complexity algorithm based on successive convex approximation technique and majorization minimization method, which is provable to eventually converge at a sub-optimal solution. Numerical results are extensively studied to corroborate that our proposed strategy outperforms the conventional designs in terms of total achievable rate and number of satisfied users.

This paper uniquely leverages unsolicited real darknet data to propose a novel system, CSC-Detector, that aims at identifying Cyber Scanning Campaigns. The latter define a new phenomenon of probing events that are distinguished by their orchestration (i.e., coordination) patterns. To achieve its aim, CSC-Detector adopts three engines. Its fingerprinting engine exploits a unique observation to extract probing activities from darknet traffic. The system's inference engine employs a set of behavioral analytics to generate numerous significant insights related to the machinery of the probing sources while its analysis engine exploits the previously obtained inferences to automatically infer the campaigns. CSC-Detector is empirically evaluated and validated using 240 GB of real darknet data. The outcome discloses 3 recent, previously unreported large-scale probing campaigns targeting diverse Internet services. Further, one of those inferred campaigns revealed that the sipscan campaign that was initially analyzed by CAIDA is arguably still active, yet operating in a stealthy, very low rate mode. We envision that the proposed system that is tailored towards darknet data, which is frequently, abundantly and effectively used to generate cyber threat intelligence, could be used by network security analysts, emergency response teams and/or observers of cyber events to infer large-scale orchestrated probing campaigns. This would be utilized for early cyber attack warning and notification as well as for simplified analysis and tracking of such events.

Network Function Virtualization (NFV) has revolutionized service provisioning in cloud datacenter networks. It enables the complete decoupling of Network Functions (NFs) from the physical hardware middle boxes that network operators deploy for implementing service-specific and strictly ordered NF chains. Precisely, NFV allows for dispatching NFs as instances of plain software called virtual network functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. Nevertheless, NF softwarization introduces processing vulnerability ( e.g. , failures caused by hardware or software, and so on). Since any failure of VNFs could break down an entire service chain, thus interrupting the service, the functionality of an NFV-enabled network will require a higher reliability compared with traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF chain placement and flow routing optimization. In order to guarantee the required reliability, an incremental approach is proposed to determine the number of required VNF backups. Through illustration, it is shown herein that the formulated single path routing model can be easily extended to support resource sharing between adjacent backup VNF instances. This paper advocates the absolute existence of a share-resource-based VNF assignment strategy that is capable of trading off all of the reliability, bandwidth, and computing resources consumption of a given service chain. A heuristic is proposed to work around the complexity of the presently formulated integer linear programming (ILP). Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of this proposed heuristic reflecting its ability to achieve very close results to those obtained through the resolution of the complex ILP within a negligible amount of time. Above and beyond, the proposed resource-sharing-based VNF placement scheme outperforms existing resource-sharing agnostic schemes by 15.6% and 14.7% in terms of bandwidth and CPU utilization respectively.