Unmanned Aerial Vehicle (UAV) swarms’ ability to dynamically adapt to communication topologies brings both great potential and significant security risks. Traditional intrusion detection systems (IDS) are designed for UAVs with fixed swarm topology and fail to secure dynamic topologies with constantly evolving communication patterns. The lack of datasets that reflect swarm behaviors across varying topologies further hinders the development of IDS. Moreover, these IDS typically focus on temporal information, overlooking the crucial spatial relationships within the UAV swarm. To address these challenges, we developed a testbed of six UAVs configured to communicate in six distinct topological graphs where each UAV acts as a node and communicates with its immediate neighbors. We then executed various cyberattacks, such as false data injection (FDI), evil twin, replay, and denial-of-service (DoS) attacks, and collected the data under both normal and attack conditions. We propose a graph neural network (GNN)-based IDS that explicitly incorporates spatial and temporal information patterns to detect intrusions. This paper seeks to answer the following questions: (a) Does exploiting spatio-temporal correlations improve detection compared to IDS trained solely on temporal data? (b) Can we develop a generalized IDS capable of detecting intrusions irrespective of the specific swarm communication topology? (c) Does the spatio-temporal correlation improve detection capabilities when the range of swarm topological data is expanded in training and the IDS is tested on unseen/new topology? Through extensive experiments on varying swarm topologies and comparison with traditional deep neural network models, we assess the effectiveness of the topology-aware GNN-based IDS in securing UAV swarm communications.

Reconfigurable intelligent surfaces (RISs) have gained considerable interest because of their inherent passive and energy-efficient design. Integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surfaces (RIS), known as UAV-RIS, can significantly improve network performance and serve as a crucial enabler for advancements in 6G mobile networks. However, ensuring security in UAV-RIS systems poses notable challenges, particularly in the presence of imperfect channel state information (CSI) and beamforming complexities. In this paper, we identify the critical security requirements for UAV-RIS beamforming in practical scenarios. To address these challenges, we introduce a novel deep deterministic policy gradient with a distributional critic (DDPG-DC)-based beamforming approach aimed at securing UAV-RIS systems while improving the overall secrecy rate. Our proposed secure beamforming solution achieves up to a 48% performance improvement compared to existing state-of-the-art algorithms.

Aerial base stations (ABSs) are expected to play a major role in 5G+ wireless networks where unmanned aerial vehicles (UAVs) serve as flying base stations to provide wireless coverage. As the utilization of ABSs continues to expand, ensuring their security and resilience against malicious attacks emerges as a vital concern. In this paper, we demonstrate successful false data injection attacks on a real UAV testbed, leading the UAV offcourse, which can impact the ABS coverage. Current research efforts focus on designing intrusion detection systems (IDS) tailored specifically for UAVs. However, the impact of adversarial attacks on UAV IDS is largely overlooked in the literature. Our results herein show that evasion attacks pose a significant threat, capable of deteriorating IDS model detection accuracy by 20 %. In this paper, we propose adopting cyber-physical fused datasets to train our proposed unsupervised sequential ensemble learningbased models to improve IDS robustness against evasion attacks. Our results, based on a practical UAV testbed and considering a wide range of evasion attacks, demonstrate that the proposed ensemble of a Transformer autoencoder and long short-term memory recurrent neural network reduces accuracy deterioration to 3 % when combined with the physical and cyber fused features.

The increasing reliance on unmanned aerial vehicles (UAVs) has escalated the associated cyber risks. While machine learning has enabled intrusion detection systems (IDSs), current IDSs do not incorporate cyber-physical UAV features, which limits their detection performance. Additionally, the lack of public UAV’s cyber and physical datasets to develop IDS hinders further research. Therefore, this paper proposes a novel IDS fusing UAV cyber and physical features to improve detection capabilities. First, we developed a testbed that includes UAV, controller, and data collection tools to execute cyber-attacks and gather cyber and physical data under normal and attack conditions. We made this dataset publicly available. The dataset covers a range of cyber-attacks including denial-of-service, replay, evil twin, and false data injection attacks. Then, machine learning-based IDSs fusing cyber and physical features were trained to detect cyber-attacks using support vector machines, feedforward neural networks, recurrent neural networks with long short-term memory cells, and convolutional neural networks. Extensive experiments were conducted on varying complexity and range of attack training data to explore whether (a) fusion of cyber and physical features enhances detection performance compared to cyber or physical features alone, (b) fusion enhances detection when IDS is trained on a single attack type and tested on unseen attacks of varying complexity, (c) fusion enhances performance when the range of attack training data increases and models are tested on unseen attacks. Answering these research questions provides insights into IDS capabilities using cyber, physical, and cyber-physical features under different conditions.