Artificial intelligence for intrusion detection systems in Unmanned Aerial Vehicles

Abstract

Unmanned Aerial Vehicles (UAVs) are seeing increased use in critical operations for law enforcement, military, industrial control surveillance and more. These hostile operating environments combined with the UAVs reliance on wireless protocols produces an increased threat level. Many attacks against the UAV are becoming commonplace as they are simple to conduct with inexpensive hardware, such as spoofing and jamming. Unfortunately, as many of these vulnerabilities exist within underlying technologies, securing the UAV becomes a difficult task. A promising approach to identifying and mitigating these attacks is the development of an intelligent intrusion detection system (IDS). The proposed approach uses principal component analysis (PCA) and one-class classifiers to detect attacks. This allows for the use of flight logs for training data, providing a versatile and ubiquitous approach. The proposed detection method is integrated into a fully developed IDS called MAVIDS. This IDS operates onboard the UAV within a resource-constrained agent device, allowing it to detect and potentially mitigate attacks even when communication to the ground control station is lost from jamming. The approach shows to be effective against GPS spoofing and jamming with macro averaged F1 scores of 90.57% and 94.3% respectively.