The ever-increasing adoption of Electric Vehicles (EVs) is transforming modern energy systems. Robust cybersecurity measures are needed to safeguard EV operations and the grid as a whole, while allowing the adoption of new paradigms, such as energy trading and Vehicle-to-grid technologies (V2G). In this work, we aim to fill the literature gap and relate security requirements with solutions. We point out key advantages and limitations of advanced security methods, specifically artificial intelligence and blockchains, in securing energy systems’ operations. First, we provide an overview of security challenges in the rapidly evolving EV ecosystem, pointing out different kinds of cyber threats targeting key components in both home and public charging infrastructures. Then, we present a review of potential solutions that utilize artificial intelligence and blockchain methods to mitigate cyber risks and recover from malicious attacks. We describe the pros and cons of AI and blockchains in addressing data confidentiality, integrity, availability, and exchange requirements. We also discuss how different technologies can be combined to leverage the unique characteristics of each in minimizing cyber risks in EV systems. Finally, the paper outlines challenges and open research directions.

In recent years, LEO satellite constellations have been used to solve many problems in communication, navigation, and observation, thanks to the low cost of launching satellites into LEO orbit and the low communication latency compared with GEO orbit. In order for LEO constellations to provide global coverage, the satellites must be able to communicate with each other, typically with a grid-like topology. In this work, we show that a static grid-like topology results in high communication latency. We propose the LEO Approximate Minimum Propagation delay (LAMP) topology, an alternative topology design method using dynamic links. The LAMP topology starts with a backbone network of persistent inter-satellite links to ensure connectivity in the constellation. Then, with the unused laser transceivers, temporary links are added such that the mean communication latency across the constellation is reduced. We show that with the LAMP topology, the average latency can be reduced by 18.5% compared to the static grid topology, and 5.61% compared to the existing Dynamic Topology of Satellites. Additionally, the LAMP topology has lower variability on mean propagation delay as the constellation parameters change compared to the static grid topology; regardless of constellation configuration, the mean propagation delay remains low.

Machine Learning (ML)-based algorithms have been widely adopted in communication networking optimization problems However, many studies that utilize ML-based approaches often overlook the comparison between ML algorithms and traditional, heuristic algorithms. In this paper, we study the merits and downsides of ML-based algorithms in a Software Defined Networking (SDN) routing scenario by analyzing a Deep Reinforcement Learning (DRL) routing algorithm assisted by a Graph Neural Network (GNN). The performances of the ML and traditional routing algorithms are evaluated in different network topologies. We consider a novel network reliability metric as well. We observe that traditional routing algorithms provide comparable performance to ML.

With the rapid advances in computing and information technologies, traditional access control models have become inadequate in terms of capturing fine-grained, and expressive security requirements of newly emerging applications. An attribute-based access control (ABAC) model provides a more flexible approach to addressing the authorization needs of complex and dynamic systems. While organizations are interested in employing newer authorization models, migrating to such models pose as a significant challenge. Many large-scale businesses need to grant authorizations to their user populations that are potentially distributed across disparate and heterogeneous computing environments. Each of these computing environments may have its own access control model. The manual development of a single policy framework for an entire organization is tedious, costly, and error-prone. In this article, we present a methodology for automatically learning ABAC policy rules from access logs of a system to simplify the policy development process. The proposed approach employs an unsupervised learning-based algorithm for detecting patterns in access logs and extracting ABAC authorization rules from these patterns. In addition, we present two policy improvement algorithms, including rule pruning and policy refinement algorithms to generate a higher quality mined policy. Finally, we implement a prototype of the proposed approach to demonstrate its feasibility.

This paper focuses on Cooperative Adaptive Cruise Control (CACC) in autonomous vehicles. In CACC, vehicles regulate their speed according to a preceding “leader” vehicle in the lane, forming a platoon. In a benign environment, CACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, CACC is vulnerable to various security threats. In this paper, we consider one of the critical threats, where the platoon leader is compromised, and forges acceleration information sent to platoon members. Such attack would lead to traffic instability and potential collisions. First, we propose information sharing in CACC model to allow vehicles and fixed infrastructure to sense and share information about platoon leaders, hence improves the reliability and supports the detection of anomalous behavior. Then, we propose a real-time anomaly detection mechanism that combines statistical learning with the physics laws of kinematics. Specifically, we propose Generalized Extreme Studentized Deviate with Sliding Chunks (GESD-SC) approach, which is applied at each vehicle in the platoon to detect anomalies in real-time based on the vehicle's own speeding decisions. Kinematic model is also utilized to detect unexpected deviations using the leader's information, communicated directly and observed by the leader's neighboring vehicle(s) and/or supporting infrastructure. Combining kinematic model with GESD-SC has shown to be effective in detecting falsification attacks in CACC. Furthermore, we analyze the time performance, and show that the proposed technique outperforms existing method in detection accuracy and processing time.

Developing automated trust mechanisms has become crucial for overcoming perceptions of uncertainty and risk by people using IoT services. Things are increasingly communicating with each other and trust in the data they deliver depends on several factors such as the links they use to communicate and the environment. This points to a need for a trust management method for "things" that considers the communication among them, environmental and security-related factors, and the net-work topology but without human intervention. To address these challenges, we propose a trust management framework that automatically computes the trust of "things". We use Multi-Attribute Decision Making (MADM) and Evidence-Based Subjective Logic (EBSL) in a trust network of "things" to take into account the uncertainty in trust values. We propose new normalization for non-monotonic attributes in MADM. We present an algorithm for automatic trust computation and evaluate its effectiveness using synthetic data and sampling from real datasets.

Cooperative Adaptive Cruise Control (CACC) is a promising application in autonomous vehicles. In CACC, a platoon is formed, where a leading vehicle sends information to regulate the speed of succeeding "following" vehicles in the road. In a benign environment, CACC provides tremendous benefits, including reducing fuel consumption, maximizing road capacity, and increasing traffic safety. However, one of the critical security threats in CACC is when a platoon has a compromised leading vehicle, which forges acceleration information sent to the platoon members. Such data falsification attack would lead to traffic instability, high fuel consumption, and potential collisions. As an effort to solve this problem, in this paper, we propose a real-time anomaly detection mechanism using physics laws of kinematics along with data fusion. The proposed technique is applied at each vehicle, where the information received from the leader is validated based on physics laws. To enhance the reliability and support the detection of anomalous behavior, we utilize information sharing in CACC by allowing vehicles and the fixed infrastructure to share sensed information about platoon leaders. In the proposed approach, each vehicle fuses information it receives to reliably detect unexpected deviations. We showed that the proposed approach is effective in detecting acceleration falsification attacks in CACC, and provides high detection accuracy (96%) and negligible false alarm rate. We compare our proposed approach with existing method and showed that the proposed approach provides superior performance in both detection accuracy and execution time.

The increased connectivity introduced in Internet of Things (IoT) applications makes such systems vulnerable to serious security threats. In this paper, we consider one of the most challenging threats in IoT networks, where devices manipulate (maliciously or unintentionally) the data transmitted in information packets as they are being forwarded from the source to the destination. We propose unsupervised learning that exploits network diversity to detect and identify suspicious networked elements. Our proposed method can identify suspicious nodes along multihop transmission paths and under variable attack levels within the network. More specifically, we formulate a contribution metric for each networked element, which is used as a feature to cluster the nodes based on their behavior. We proposed two detection approaches, namely hard detection and soft detection. In the former, nodes are clustered into malicious or benign group; while in the latter, nodes are clustered into three groups based on their suspicious level, then highly suspicious nodes are discarded and more accurate contribution features are evaluated for the remaining nodes. Soft detection has higher detection accuracy provided that there is sufficient network diversity. Simulation results show that the proposed methods achieve high detection accuracy under different percentages of malicious nodes in the network and in the existence of channel errors.

This paper introduces the mobile access coordinated wireless sensor network (MC-WSN)-a novel energy efficient scheme for time-sensitive applications. In conventional sensor networks with mobile access points (SENMA), the mobile access points (MAs) traverse the network to collect information directly from individual sensors. While simplifying the routing process, a major limitation with SENMA is that data transmission is limited by the physical speed of the MAs and their trajectory length, resulting in low throughput and large delay. In an effort to resolve this problem, we introduce the MC-WSN architecture, for which a major feature is that: through active network deployment and topology design, the number of hops from any sensor to the MA can be limited to a pre-specified number. In this paper, we investigate the optimal topology design that minimizes the average number of hops from sensor to MA, and provide the throughput analysis under both single-path and multipath routing cases. Moreover, putting MC-WSN in the bigger picture of network design and development, we provide a unified framework for wireless network modeling and characterization. Under this general framework, it can be seen that MC-WSN reflects the integration of structure-ensured reliability/efficiency and ad-hoc enabled flexibility.

This paper considers reliable multihop transmission under routing attacks, where a malicious relay can modify or drop a packet as it is being forwarded to the destination. We propose a transmission scheme that detects malicious nodes launching routing attacks through incorporating diversity over multi-layer relays, where each relay can establish a direct connection with relays at preceding and succeeding hop levels. We prove that the proposed approach can efficiently detect malicious nodes, provided that there is at least one honest relay at each hop level. We highlight the trade-off between network efficiency and security, and show the impact of the diversity level and the number of hops on the network performance through theoretical analysis and simulation examples. Our results provide insights on general network architecture development and topology design.

In this paper, we analyze the throughput of a novel mobile access coordinated wireless sensor network architecture (MC-WSN) under single path and multipath routing. The obtained throughput expressions highlight the trade-off between achieving high throughput performance and improving the network security strength. The results reveal the importance of: (i) minimizing the number of hops in maximizing the throughput, and (ii) adopting routing diversity in combating malicious attacks and network failure conditions. We control the number of hops in data transmission through optimal topology design and active network deployment achieved by the mobile access point (MA). To combat routing attacks, we propose a secure routing path selection approach, and show the impact of the proposed approach on improving the throughput performance under malicious attacks.

This paper considers primary user emulation attacks in cognitive radio networks operating in the white spaces of the digital TV (DTV) band. We propose a reliable AES-assisted DTV scheme, in which an AES-encrypted reference signal is generated at the TV transmitter and used as the sync bits of the DTV data frames. By allowing a shared secret between the transmitter and the receiver, the reference signal can be regenerated at the receiver and used to achieve accurate identification of the authorized primary users. In addition, when combined with the analysis on the autocorrelation of the received signal, the presence of the malicious user can be detected accurately whether or not the primary user is present. We analyze the effectiveness of the proposed approach through both theoretical analysis and simulation examples. It is shown that with the AES-assisted DTV scheme, the primary user, as well as malicious user, can be detected with high accuracy under primary user emulation attacks. It should be emphasized that the proposed scheme requires no changes in hardware or system structure except for a plug-in AES chip. Potentially, it can be applied directly to today's DTV system under primary user emulation attacks for more efficient spectrum sharing.

This paper explores reliable data fusion in mobile access wireless sensor networks under Byzantine attacks. We consider the q-out-of-m rule, which is popular in distributed detection and can achieve a good tradeoff between the miss detection probability and the false alarm rate. However, a major limitation with it is that the optimal scheme parameters can only be obtained through exhaustive search, making it infeasible for large networks. In this paper, first, by exploiting the linear relationship between the scheme parameters and the network size, we propose simple but effective sub-optimal linear approaches. Second, for better flexibility and scalability, we derive a near-optimal closed-form solution based on the central limit theorem. Third, subjecting to a miss detection constraint, we prove that the false alarm rate of q-out-of-m diminishes exponentially as the network size increases, even if the percentage of malicious nodes remains fixed. Finally, we propose an effective malicious node detection scheme for adaptive data fusion under time-varying attacks; the proposed scheme is analyzed using the entropy-based trust model, and shown to be optimal from the information theory point of view. Simulation examples are provided to illustrate the performance of proposed approaches under both static and dynamic attacks.

This paper considers primary user emulation attacks (PUEA) in cognitive radio networks operating in the white spaces of the digital TV (DTV) band. We propose a reliable AES-encrypted DTV scheme, in which an AES-encrypted reference signal is generated at the TV transmitter and used as the sync bytes of each DTV data frame. By allowing a shared secret between the transmitter and the receiver, the reference signal can be regenerated at the receiver and be used to achieve accurate identification of authorized primary users. We analyze the effectiveness of the proposed approach through both theoretical derivation and simulation examples. It is shown that with the AES-encrypted DTV scheme, the primary user can be detected with high accuracy and low false alarm rate under primary user emulation attacks. It should be emphasized that the proposed scheme requires no changes in hardware or system structure except of a plug-in AES chip. Potentially, it can be applied to today's DTV system directly to mitigate primary user emulation attacks, and achieve efficient spectrum sharing.

This paper considers architecture design of mobile access coordinated wireless sensor networks (MC-WSN) for reliable and efficient information exchange. In sensor networks with mobile access points (SENMA), the mobile access points collect information directly from individual sensors as they traverse the network, such that no routing is needed in data transmission. While being energy efficient, a major limitation with SENMA is the large delay in data collection, making it undesirable for timesensitive applications. In the proposed MC-WSN architecture, the sensor network is coordinated by powerful mobile access points (MA), such that the number of hops from each sensor to the MA is minimized and limited to a prespecified number through active network deployment and network topology design. Unlike in SENMA, where the data collection delay depends on the physical speed of the MA, in MC-WSN, the delay depends on the number of hops and the electromagnetic wave speed, and is independent of the physical speed of the MA. This innovative architecture is energy efficient, resilient, fast reacting and can actively prolong the lifetime of sensor networks. Our simulations show that the proposed MC-WSN can achieve higher energy-efficiency and orders of magnitude lower delay over SENMA, especially for large-scale networks.

Along with the advent of reconfigurable radios, hostile jamming is no longer limited to military applications, but has become a serious threat for civilian wireless communications, where OFDM has been identified as one of the most efficient transmission technologies. In this paper, we consider reliable transmission of OFDM systems under multi-tone jamming. We propose to enhance the jamming resistance of OFDM through symbol level precoding. Our approach is to find the optimal precoder and decoder that can minimize the MSE between the transmitted and the estimated symbols, subject to a given transmit power constraint. Closed-form solutions are derived, and further demonstrated through simulation examples. It is observed that adding controlled redundancy at symbol level is an effective way to mitigate hostile jamming in OFDM systems.

This paper considers cooperative sensing in cognitive networks under Spectrum Sensing Data Falsification attack (SSDF) in which malicious users can intentionally send false sensing information. One effective method to deal with the SSDF attack is the q-out-of-m scheme, where the sensing decision is based on q sensing reports out of m polled nodes. The major limitation with the q-out-of-m scheme is its high computational complexity due to exhaustive search. In this paper, we prove that for a fixed percentage of malicious users, the detection accuracy increases almost exponentially as the network size increases. Motivated by this observation, as well as the linear relationship between the scheme parameters and the network size, we propose a simple but accurate approach that significantly reduces the complexity of the q-out-of-m scheme. The proposed approach can easily be applied to the large scale networks, which can be much more reliable under malicious attacks.

A scheme is proposed for adaptively changing the code rate of coded OFDMA systems via changing the puncturing rate within a single codeword (SCW). In the proposed structure, the data is encoded with the lowest available code rate then it is divided among different resource blocks (tiles) where it is punctured adaptively based on some measure of the channel quality for each tile. The proposed scheme is compared against using multiple codewords (MCWs) where the transmitter divides the data over tiles and encodes them separately. We investigate two different adaptive modulation and coding (AMC) selection methods. The first is a recursive scheme that operates directly on the SNR whereas the second operates on the effective SNR value that is obtained using Mutual Information Effective SNR Mapping (MIESM). We then compare our scheme to Per-Frame Adaptation (PFA) where we fix the modulation and coding scheme (MCS) over a given frame. We show via simulations that when using the recursive rate selection method the SCW scheme significantly outperforms the MCWs and the PFA. It is also shown that applying the MIESM rate selection method, the PFA improves significantly, yet the SCW scheme is the best performer. We also introduce a novel interleaving method prior to puncturing that improves the performance for certain restricted adaptation mechanisms.