A Unified Time Series Analytics based Intrusion Detection Framework for CAN BUS Attacks

Modern smart vehicles have a Controller Area Network (CAN) that supports intra-vehicle communication between intelligent Electronic Control Units (ECUs). The CAN is known to be vulnerable to various cyber attacks. In this paper, we propose a unified framework that can detect multiple types of cyber attacks (viz., Denial of Service, Fuzzy, Impersonation) affecting the CAN. Specifically, we construct a feature by observing the timing information of CAN packets exchanged over the CAN bus network over partitioned time windows to construct a low dimensional representation of the entire CAN network as a time series latent space. Then, we apply a two tier anomaly based intrusion detection model that keeps track of short term and long term memory of deviations in the initial time series latent space, to create a 'stateful latent space'. Then, we learn the boundaries of the benign stateful latent space that specify the attack detection criterion. To find hyper-parameters of our proposed model, we formulate a preference based multi-objective optimization problem that optimizes security objectives tailored for a network-wide time series anomaly based intrusion detector by balancing trade-offs between false alarm count, time to detection, and missed detection rate. We use real benign and attack datasets collected from a Kia Soul vehicle to validate our framework and show how our performance outperforms existing works.