CAHOOT: a Context-Aware veHicular intrusiOn detectiOn sysTem

Software in modern vehicles is becoming increasingly complex and subject to vulnerabilities that an intruder can exploit to alter the functionality of vehicles. To this purpose, we introduce CAHOOT, a novel context-aware Intrusion Detection System (IDS) capable of detecting potential intrusions in both human and autonomous driving modes. In CAHOOT, context information consists of data collected at run-time by vehicle's sensors and engine. Such information is used to determine drivers' habits and information related to the environment, like traffic conditions. In this paper, we create and use a dataset by using a customised version of the MetaDrive simulator capable of collecting both human and AI driving data. Then we simulate several types of intrusions while driving: denial of service, spoofing and replay attacks. As a final step, we use the generated dataset to evaluate the CAHOOT algorithm by using several machine learning methods. The results show that CAHOOT is extremely reliable in detecting intrusions.