Behaviour-Based Anomaly Detection of Cyber-Physical Attacks on a Robotic Vehicle

Security is one of the key challenges in cyberphysical systems, because by their nature, any cyber attack against them can have physical repercussions. This is a critical issue for autonomous vehicles; if compromised in terms of their communications or computation they can cause considerable physical damage due to their mobility. Our aim here is to facilitate the automatic detection of cyber attacks on a robotic vehicle. For this purpose, we have developed a detection mechanism, which monitors real-time data from a large number of sources onboard the vehicle, including its sensors, networks and processing. Following a learning phase, where the vehicle is trained in a non-attack state on what values are considered normal, it is then subjected to a series of different cyber-physical and physical-cyber attacks. We approach the problem as a binary classification problem of whether the robot is able to self-detect when and whether it is under attack. Our experimental results show that the approach is promising for most attacks that the vehicle is subjected to. We further improve its performance by using weights that accentuate the anomalies that are less common thus improving overall performance of the detection mechanism for unknown attacks.