Domain Adversarial Neural Network-Based Intrusion Detection System for In-Vehicle Network Variant Attacks

Abstract

A controller area network (CAN) bus that controls real-time communication and data transmission of electronic control units in vehicles lacks security mechanisms and is highly vulnerable to attacks. The detection effectiveness of existing In-Vehicle network intrusion detection systems (IDSs) is usually reliant on training samples of known attacks. Owing to the simple structure of the CAN bus protocol, attackers can easily create variants based on known attacks. In this study, we propose a CAN bus IDS based on a domain adversarial neural network, which can achieve high detection performance on unlearned variant attack data. In addition, we used feature fusion techniques to synthetically extract the features of the attack data to improve detection capability. The experimental results demonstrate that our proposed model has high performance and generalization ability in attack detection.