Seeing the unseen: Intrusion attack detection in connected autonomous vehicles

Connected and autonomous vehicles (CAVs) are becoming increasingly common, and their popularity is expected to increase further, especially due to the ease and convenience they provide. An important component of CAVs is the Electronic Control Unit or the ECU; these ECUs are connected to each other through the Controller Area Network or CAN bus. The increasing use of CAVs has significantly expanded its threat landscape with several security vulnerabilities that can be severely damaging, especially when the ECUs or CAN are compromised. Existing security mechanisms are largely designed for known vulnerabilities, leaving the potential for unknown attacks high. In order to address this gap in detecting previously unknown attacks, we propose a novel autoencoder and payload fragmentation-based in-vehicle unknown intrusion method. For the precise prediction of attack and sub-attack, the data payload is fragmented into one-byte fragments, and the prediction dependency is analyzed for each fragment. Extensive evaluations demonstrate that our proposed method shows improved performance with an attack classification accuracy of 99.88% and sub-attack accuracy of 99.98%.