Risk-Aware Intrusion Detection and Prevention System for Automated UAS

This paper designs a proof-of-concept for a Deep Learning-based IDS for UAS. As the drone market grows, safety becomes crucial. Unmanned Aircraft System (UAS) attacks can endanger lives and facilities. With the increasing complexity of attacks, detection has become challenging. Machine Learningbased Intrusion Detection System (IDS), trained on the CSE-CIC-IDS2018 dataset, can handle defined attacks. Combining IDS with an Intrusion Prevention System(IPS), using Threat Analysis and Risk Assessment (TARA) from the automotive domain ensures the system's safety even after attacks. The implementation involves Raspberry Pi as an attacker and defender. The ISO/SAE 21434 standard serves as the foundation for cybersecurity adaptation.