A GRU-Based Lightweight System for CAN Intrusion Detection in Real Time
Haoyu Ma, Jianqiu Cao, Bo Mi, Darong Huang, Yang Liu, Shaoqian Li
Abstract
With the rapid development of vehicular networking and intelligence, more interfaces are adopted by cars to interact with the external world. Accordingly, this also brings enormous security risks, which are potentially catastrophic due to communication loopholes. Since the Controller Area Network (CAN) is critical to the transmission of commands among vehicular components, it has become a prime target for hacker research and attack. Considering that the CAN bus is commonly used and its protocol is always flawed, how to efficiently detect the intrusions against it has become an evitable problem. In this paper, we presented an intrusion detection system that can be rapidly deployed inside the vehicle. Aiming at achieving the goal of real-time detection, we devised a feature extraction algorithm with low complexity and thoroughly exploited its advantages via a GRU-based lightweight neural network. The experiment was physically conducted on in-vehicle embedded devices using publicly available datasets. Experiment results illustrated that our intrusion detection system could be rapidly deployed with high classification and real-time performance. Moreover, we also discussed how an intrusion detection system could work with OTA services to improve the intelligence of vehicular operating systems and prevent potential attacks.