Real-time attack detection system using unsupervised learning for accident prevention

Autonomous systems are increasingly exposed to emergent security threats that can impact their operational safety. Although anomaly detection techniques are widely used to identify these attacks, they are mostly validated in offline settings. This paper introduces an innovative attack detection system that leverages unsupervised learning for real-time deployment. The effectiveness of this system is confirmed through experimental validation on unmanned aerial vehicles during their operation.