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Detecting spoofing attacks on the positions of unmanned aerial vehicles(UAVs) within a swarm is challenging. Traditional methods relying solely onindividually reported positions and pairwise distance measurements areineffective in identifying the misbehavior of malicious UAVs. This paperpresents a novel systematic structure designed to detect and mitigate spoofingattacks in UAV swarms. We formulate the problem of detecting malicious UAVs asa localization feasibility problem, leveraging the reported positions anddistance measurements. To address this problem, we develop a semidefiniterelaxation (SDR) approach, which reformulates the non-convex localizationproblem into a convex and tractable semidefinite program (SDP). Additionally,we propose two innovative algorithms that leverage the proximity of neighboringUAVs to identify malicious UAVs effectively. Simulations demonstrate thesuperior performance of our proposed approaches compared to existingbenchmarks. Our methods exhibit robustness across various swarm networks,showcasing their effectiveness in detecting and mitigating spoofing attacks.{\blue Specifically, the detection success rate is improved by up to 65\%,55\%, and 51\% against distributed, collusion, and mixed attacks, respectively,compared to the benchmarks.

Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations