Performance evaluation of cyber-physical intrusion detection on a robotic vehicle

Intrusion detection systems designed for conventional computer systems and networks are not necessarily suitable for mobile cyber-physical systems, such as robots, drones and automobiles. They tend to be geared towards attacks of different nature and do not take into account mobility, energy consumption and other physical aspects that are vital to a mobile cyber-physical system. We have developed a decision tree-based method for detecting cyber attacks on a small-scale robotic vehicle using both cyber and physical features that can be measured by its on-board systems and processes. We evaluate it experimentally against a variety of scenarios involving denial of service, command injection and two types of malware attacks. We observe that the addition of physical features noticeably improves the detection accuracy for two of the four attack types and reduces the detection latency for all four.