LIDS: A Lightweight Intrusion Detection System for Controller Area Network
Zhangwei Yu, Yan Liu, Renfa Li, Wanli Chang · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2025
Controller area network (CAN) is widely adopted in automobiles and susceptible to cyber attacks with the development of intelligent connected vehicles. While neural networks have demonstrated high accuracy in detection of such attacks, they consume a large amount of resources, hence unsuitable to be directly used for the automotive domain. In this work, we propose a lightweight intrusion detection system (LIDS) for CAN. It first filters out denial-of-service (DoS) and Fuzzy attacks through list screening, following which, a multilayer perceptron (MLP) model is deployed to predict Impersonation attacks. Leveraging this combination, the detection accuracy is kept and the resources required are significantly reduced. LIDS is able to run on small hardware with 520-kB memory and CPU of 240 MHz. Its power consumption is one order of magnitude lower than the existing works, thus an excellent candidate for protection of CAN in automobiles.