Authors: Thi-Thu-Huong Le, Naufal Suryanto, Howon Kim, Janghyun Ji and Shinwook Heo

Conference: The 12th International Symposium on Information and Communication Technology (SOICT 2023)

Abstract: In modern automobiles, the reliance on vehicle Controller Area Network (CAN) networks has surged, underlining the paramount significance of safeguarding these networks against intrusions. In this work, we unveil an innovative approach for intrusion detection and explanation within in-vehicle CAN networks, employing the formidable synergy of Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Our method is tailored to address network imbalance challenges, offering prowess in binary and multiclass classification tasks. Integral to our approach is the seamless integration of SHAP values, serving as illuminating guides that unravel the intricacies of detected intrusions. This fusion elevates the system's interpretability, equipping stakeholders with deeper insights. Our contribution is underpinned by a rigorous evaluation of our approach, featuring a comprehensive analysis of a published dataset alongside comparisons with established literature. The results underscore the exceptional efficacy of our method, showcasing its remarkable accuracy in detecting intrusions. However, the essence of our methodology transcends mere detection precision. The explanatory capabilities of SHAP values come to the forefront. This augments both understanding and decision-making into the contributing factors behind the detected intrusion classification model.

Link: hhttp://camps.aptaracorp.com/ACM_PMS/PMS/ACM/SOICT2023/107/2b10c696-6f69-11ee-b37c-16bb50361d1f/OUT/soict2023-107.html