DATI-IDS: Domain Adaptation and Time-Series Imaging-Based Intrusion Detection System for Connected Autonomous Vehicles

With the advancement of artificial intelligence, automobiles are progressively transitioning from traditional mechanization to Connected Autonomous Vehicles (CAVs), significantly enhancing driving comfort and safety. As the standard communication protocol in CAVs, the Controller Area Network (CAN) remains vulnerable to attacks due to the lack of robust security mechanisms. While existing deep learning-based vehicle network intrusion detection systems can effectively identify known attacks, their ability to detect unknown attacks is limited due to the same data distribution in the source and target domain. To address this issue, we propose a domain adaptation and time-series imaging-based intrusion detection system (DATI-IDS) to detect known and unknown attacks, where the deep domain adaptation method is used to solve the source and target domain data distribution difference problem by optimizing the multiple kernel maximum mean discrepancy (MK-MMD) between the source domain and target domain images and the classification loss, and the time-series imaging method is used to capture temporal dependencies and improve efficiency by transforming the CAN ID sequence into a two-dimensional gramian angular summation field (GASF) image. The effectiveness of the proposed model is evaluated across nine distinct unknown attack scenarios using the Car-Hacking dataset and the survival analysis dataset. Comparative analysis with previous studies demonstrates superior performance, faster inference times, and reduced model complexity.