In the realm of unmanned aerial vehicle (UAV) communication, the utilization of UAVs as aerial relays for ground nodes (GNs) introduces strategic flexibility, especially in scenarios where ground base stations may experience unforeseen impairments. However, this form of communication is vulnerable to eavesdropping by malicious entities due to the broadcast nature of wireless channels. In this paper, we tackle this problem by introducing a spatiotemporal diversification-based artificial noise (AN) injection strategy, known as moving target defense (MTD), aiming to confuse potential attackers without compromising legitimate communications. The proposed approach targets maximizing the average secrecy rate (ASR) by jointly optimizing the UAV's trajectory and transmit power, aligned with optimizing the MTD transmit power splitting factor between legitimate and AN signals at the GN source. The formulated problem is a non-convex mixed integer nonlinear programming (MINLP) problem due to the non-convexity of the secrecy rate. To solve it, we formulate our system as a Markov decision process, and then we propose a novel deep reinforcement learning (DRL)-based approach to enhance the ASR under various system constraints. Numerical results demonstrate the superiority of the proposed algorithm over benchmarks in terms of ASR and intercept probability, showcasing its effectiveness in enhancing the security of UAV-assisted communications.

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency needs are challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense (MTD). Driven by deep reinforcement learning (DRL) and realtime monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user's quality of experience (QoE), reduce the overall latency, and minimize the attacker's intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE outperforming conventional Q-learning algorithms, making it suitable for mission-critical XR applications.

Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task, due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. The moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions, based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of the network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks has several limitations, such as they are not well-suited for highly dynamic environments; they require high synchronization modules that are resource-intensive and difficult to implement; and finally, they rely heavily on the attack-defense models, which may not always be accurate or comprehensive to use. In this paper, we propose an intelligent spatiotemporal diversification MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. Specifically, we design benign random data injection patterns to meet the security and reliability requirements of the vehicular network. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. In order to optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are conducted to demonstrate the effectiveness of our proposed solution where they improved the system security by almost 28% and 49%, respectively compared to the conventional relay selection approach.

Fifth-generation and beyond (5G and xG) wireless networks are envisioned to meet the requirements of vertical applications like high traffic throughput, ultra-massive connectivity, extremely low latency, and high quality of service. Disruptive technologies, such as massive multiple-input multiple-output, millimeter wave, and multiple access are being deployed to meet these requirements. However, their deployment poses several challenges, including a lack of network transparency, management decentralization, and reliability. Moreover, the heterogeneity of future networks raises security concerns, e.g., confidentiality, privacy, and trustworthiness. Indeed, due to the emergence of novel paradigms, e.g., quantum computing, traditional security approaches are no longer sufficient to protect over-the-air communications. Hence, 5G/xG networks must consider smart security techniques to operate seamlessly and efficiently. Within this context, physical layer security (PLS) and blockchain represent promising solutions to complement existing methods. By exploiting the characteristics of wireless links, PLS can enhance the security of communications, while blockchain may enable networks’ decentralization, integrity, and trustworthiness. Motivated by these advancements, we provide an in-depth review of the existing PLS and blockchain literature. Then, for the first time in the literature, we present a framework to integrate PLS with blockchain in 5G/xG systems. We first provide a thorough discussion about the potential of PLS and blockchain for 5G/xG systems. Then, we present our vision of a cross-layer architecture that leverages PLS and blockchain. Through a case study, we demonstrate the high potential of cross-layer design to improve the security of vehicular networks. Finally, we identify related challenges and shed light on future research directions.

Offloading computation to Mobile Edge Vehicular Clouds (MeVC) is an effective mechanism to meet the expectations of the \textcolor{blue}{next-generation} IoT networks. MeVC applications \textcolor{blue}{rely} heavily on resilient, trusted, and reliable wireless communication as a pillar to support its operation. This paper presents a software-defined, flexible vehicular Ad-hoc NEtwork (Sd-VANET) architecture that enables the construction of resilient network infrastructure and the secure operation of MeVC. The Sd-VANET is managed by a novel situation-aware blockchain-based framework that dynamically reprograms the network topology and data flows at run-time to enable Moving-target Defense (MtD) applications against attacks and failures. The presented solution was mathematically modeled and analyzed. Simulations showed that the proposed system can effectively and efficiently improve the quality of communication services and their resilience against attacks and failures.

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

The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs’ secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of the SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. Furthermore, we develop an offline blockchain framework to store the information of participating relays and make it available for future operations, in order to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. The stored reputations of the participating relays are used to develop a self-learning algorithm to exclude the non-reputable relays from the selection group. Finally, we present thorough numerical results to demonstrate the superiority of the proposed system in terms of security, credibility, and integrity.