Collaborative Intrusion Detection System for Unmanned Aerial Vehicles Swarm Security

Unmanned Aerial Vehicles (UAVs) are increasingly used in military, civil, and commercial applications, but they are vulnerable to cyberattacks that threaten security and privacy. These threats include signal interception, unauthorized access, data theft, and remote control of UAVs. An Intrusion Detection System (IDS) is used to mitigate these risks. However, most IDS focus on individual data sources, failing to detect swarm-specific threats. This work presents REMY, a collaboRative intrusion dEtection system for unManned aerial vehicles swarm SecuritY that detects network and in-flight anomalies using machine learning techniques. REMY identifies network attacks such as blackhole, grayhole, and flooding, as well as in-flight threats like GPS spoofing and jamming. Federated learning is applied in model training to ensure data privacy and collaboration. The system is designed to be hardware-independent and lightweight, with low energy consumption and efficient use of resources.