Unmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the most lethal cyber-attacks that can target an UAV network, namely, false information dissemination, GPS spoofing, jamming, and black hole and gray hole attacks. Extensive simulations confirm that the proposed scheme performs well in terms of attack detection even with a large number of UAVs and attackers since it exhibits a high detection rate, a low number of false positives, and prompt detection with a low communication overhead.

Advances in wireless communications and microelectronics have spearheaded the development of unmanned aerial vehicles (UAVs), which can be used to augment a ground network composed of sensors and/or vehicles in order to increase coverage, enhance the end-to-end delay, and improve data processing. While UAV-aided networks can potentially find applications in many areas, a number of issues, particularly security, have not been readily addressed. The intrusion detection system is the most commonly used technique to detect attackers. In this paper, we focus on addressing two main issues within the context of intrusion detection and attacker ejection in UAV-aided networks, namely, activation of the intrusion monitoring process and attacker ejection. In fact, when a large number of nodes activate their monitoring processes, the incurred overhead can be substantial and, as a consequence, degrades the network performance. Therefore, a tradeoff between the intrusion detection rate and overhead is considered in this work. It is not always the best strategy to eject a node immediately when it exhibits a bad sign of malicious activities since this sign could be provisional (the node may switch to a normal behavior in the future) or be simply due to noise or unreliable communications. Thus, a dilemma between detection and false positive rates is taken into account in this paper. We propose to address these two security issues by a Bayesian game model in order to accurately detect attacks (i.e., high detection and low false positive rates) with a low overhead. Simulation results have demonstrated that our proposed security game framework does achieve reliable detection.

Recent advances in edge computing and its imminent adoption in Sixth Generation (6G) wireless networks have led to a propitious paradigm defined as 6G edge computing. However, due to the communication mode and network virtualization of the 6G architecture, 6G edge computing presents several security concerns, including attack monitoring and detection. In this article, we present a new and robust Zero Trust Architecture (ZTA) empowered intrusion detection framework to protect 6G edge computing from network attacks. The proposed detection framework executes detection techniques based on security rules and machine learning algorithms as well as detection strategies based on a standalone and collaborative process with the goal to harden the security and hence to detect accurately the attacks. Furthermore, we propose a dynamic ZTA based on a non-cooperative game to harden further the security of 6G edge computing networks, with consideration of detection accuracy in terms of false positives and false negatives, and network cost as a computational overhead.

The Sixth Generation Heterogeneous Network (6G HetNet) is a global interconnected system that serves a myriad variety of applications and services across multiple domains such as satellite, air, ground, and underwater networks. It provides a platform for the development of novel Internet of Things (IoT) applications and services, particularly for the Internet of Vehicles (IoV), which encompasses all devices involved in intra-vehicle and inter-vehicle communications. However, this evolution towards a unified and huge cellular infrastructure creates new security challenges that require an intelligent attack detection framework to safeguard the network against cyber-security threats. This paper proposes a hierarchical attack detection framework for 6G-enabled IoV. This framework relies on the processing capacities of edge nodes to satisfy the main 6G Key Performance Indicators (KPIs), such as trustworthiness, latency, connectivity, data rate and energy consumption. Federated Learning (FL) and non-cooperative gaming are used to train attack models and improve the detection process over time. The cooperative detection process based on FL is executed by security entities, IoV devices, edge servers and Security Information and Event Management (SIEM) to improve the detection accuracy over time. To harden the security of the proposed attack detection framework, a robust Stackelberg security game is developed to identify malicious IoV devices and edge servers, and select suitable IoV devices and edge servers to participate in the training and attack detection processes. The identification and selection process mainly relies on computing a reputation score based on the activities of these IoV devices and edge servers. As compared to current security monitoring and detection solutions, our framework balances detection accuracy and reduced network overhead, specifically as the system scales up, i.e., when the malicious traffic is high. In addition, it mitigates threats from both external and internal adversaries