Mobile Edge Assisted Literal Multi-Dimensional Anomaly Detection of In-Vehicle Network Using LSTM

The development of Internet of Vehicles (IoVs) have introduced more intrusions to the vulnerabilities of in-vehicle network. It is important to detect in-vehicle network anomaly for the purpose of driving safety. The previous studies either focus on one-dimension anomaly behavior of in-vehicle network or looks into the semantics of in-vehicle network, which is neither effective nor efficient for vehicle pilot. In this paper, we propose a literal multi-dimensional anomaly detection approach using the distributed long-short-term-memory (LSTM) framework for in-vehicle network, especially the Control Area Network (CAN). The proposed approach only needs the literal binary CAN message instead of revealing the semantics of CAN message. To enhance the accuracy and efficiency of detection, it detects anomaly from both time and data dimension simultaneously by exploiting multi-task LSTM neural network on mobile edge. The extensive evaluation results show that the proposed anomaly detection achieves a satisfying accuracy of 90%. The detection speed is as fast as 0.61 ms on mobile edge.