  Detecting spoofing attacks on the positions of unmanned aerial vehicles
(UAVs) within a swarm is challenging. Traditional methods relying solely on
individually reported positions and pairwise distance measurements are
ineffective in identifying the misbehavior of malicious UAVs. This paper
presents a novel systematic structure designed to detect and mitigate spoofing
attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as
a localization feasibility problem, leveraging the reported positions and
distance measurements. To address this problem, we develop a semidefinite
relaxation (SDR) approach, which reformulates the non-convex localization
problem into a convex and tractable semidefinite program (SDP). Additionally,
we propose two innovative algorithms that leverage the proximity of neighboring
UAVs to identify malicious UAVs effectively. Simulations demonstrate the
superior performance of our proposed approaches compared to existing
benchmarks. 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.
