Efficient and Effective In-Vehicle Intrusion Detection System using Binarized Convolutional Neural Network

Modern vehicles are equipped with multiple Electronic Control Units (ECUs) communicating over in-vehicle networks such as Controller Area Network (CAN). Inherent security limitations in CAN necessitate the use of Intrusion Detection Systems (IDSs) for protection against potential threats. While some IDSs leverage advanced deep learning to improve accuracy, issues such as long processing time and large memory size remain. Existing Binarized Neural Network (BNN)-based IDSs, proposed as a solution for efficiency, often compromise on accuracy. To this end, we introduce a novel Binarized Convolutional Neural Network (BCNN)-based IDS, designed to exploit the temporal and spatial characteristics of CAN messages to achieve both efficiency and detection accuracy. In particular, our approach includes a novel input generator capturing temporal and spatial correlations of messages, aiding model learning and ensuring high-accuracy performance. Experimental results suggest our IDS effectively reduces memory utilization and detection latency while maintaining high detection rates. Our IDS runs 4 times faster and utilizes only 3.3% of the memory space required by a full-precision CNN-based IDS. Meanwhile, our proposed system demonstrates a detection accuracy between 94.19% and 96.82% relative to the CNN-based IDS across different attack scenarios. This performance marks a noteworthy improvement over existing state-of-the-art BNN-based IDS designs.