This paper focuses on the challenge-response physical-layer authentication (CR-PLA) scheme where a reflecting intelligent surface (RIS) is under the control of a receiving base station (BS) (Bob) who aims at checking if received messages come from a legitimate user equipment (UE) Alice or from an impersonating device (Trudy). To this end, Bob sets a random configuration of the RIS which remains secret to the attacker, and verifies that the channel estimated on the received message corresponds to the set configuration. We design the probability distribution of RIS configurations chosen by the verifier to maximize average capacity while satisfying an upper bound on missed detection (MD) probability for a given false alarm (FA) probability. The balance of communication and security metrics demonstrated by the numerical results shows the effectiveness and potential of the CR-PLA scheme.

Jamming attacks pose a critical threat to wireless networks, particularly in cell-free massive MIMO systems, where distributed access points and user equipment (UE) create complex, time-varying topologies. This paper proposes a novel jamming detection framework leveraging dynamic graphs and graph convolution neural networks (GCN) to address this challenge. By modeling the network as a dynamic graph, we capture evolving communication links and detect jamming attacks as anomalies in the graph evolution. A GCN-Transformers-based model, trained with supervised learning, learns graph embeddings to identify malicious interference. Performance evaluation in simulated scenarios with moving UEs, varying jamming conditions and channel fadings, demonstrates the method’s effectiveness, which is assessed through accuracy and F1 score metrics, achieving promising results for effective jamming detection.

We analyze a sensing system where multiple sources transmit status updates to a common receiver. We assume that the correlation of transmitted information allows updates from one source to enhance the information freshness of others. We study the objective of minimizing individual information staleness, quantified by the Age of Information (AoI), at the receiver's end. We evaluate both centralized and distributed optimization strategies. In the former case, we select the globally optimal transmission rates for each source to minimize the total average AoI of the system. For distributed optimization, sources are seen as players in a non-cooperative game of complete information, for which we compute the Nash equilibria. As an example, we consider a fixed correlation budget shared among two sources and evaluate the transmission rates depending on the specific level of correlation. Our results show that, under a centralized approach, it is convenient that only the source with more influential content transmits, while the other source reduces its data injection rate. In contrast, independent transmission in a distributed setup leads to greater congestion and higher average AoI. However, as correlation increases, the performance of the distributed system approaches that of the centralized model, indicating that decentralized management becomes effective in highly correlated scenarios.

Anti-spoofing techniques for current global navigation satellite systems (GNSS) authenticate signals on a single band and from a single system. However, nowadays commercial GNSS receivers commonly calculate the position, velocity, and time (PVT) solution by simultaneously utilizing signals from multiple constellations and bands, with a substantial enhancement in both accuracy and availability. Therefore, anti-spoofing techniques have recently been proposed that mix authenticated and non-authenticated signals to increase performance without sacrificing security. In this paper, we formalize the models of such signal mixture-based authentication checks. We propose a spoofing attack generating a fake signal that leads the victim to a target PVT solution, undetected. We analytically relate the degrees of freedom of the attacker in manipulating the victim’s solution to both the employed security checks and the number of open non-authenticated signals that can be tampered with by the attacker. The performance of the considered attack strategies are tested on an experimental dataset. Finally, we assess the limits of PVT-based GNSS authentication checks where both authenticated and non-authenticated signals are used.

The threat of signal spoofing attacks against global navigation satellite system (GNSS) has grown in recent years and has motivated the study of anti-spoofing techniques. However, defense methods have been designed only against specific attacks. This paper introduces a general model of the spoofing attack framework in GNSS, from which optimal attack and defense strategies are derived. We consider a scenario with a legitimate receiver (Bob) testing if the received signals come from multiple legitimate space vehicles (Alice) or from an attack device (Eve). We first derive the optimal attack strategy against a Gaussian transmission from Alice, by minimizing an outer bound on the achievable error probability region of the spoofing detection test. Then, framing the spoofing and its detection as an adversarial game, we show that the Gaussian transmission and the corresponding optimal attack constitute a Nash equilibrium. Lastly, we consider the case of practical modulation schemes for Alice and derive the generalized likelihood ratio test. Numerical results validate the analytical derivations and show that the bound on the achievable error region is representative of the actual performance.