Intrusion Detection for Intelligent Transportation Systems: A lightweight interpretable model

Abstract

Intrusion detection systems (IDSs) are essential in Intelligent transportation system (ITS) for detecting and identifying malicious activities. Although deep learning is commonly used in IDS, its limited interpretability hinders large-scale deployment. Additionally, the computational and storage constraints of ITS devices pose challenges to traditional IDS solutions. To address these issues, we propose RK-IDS, an interpretable IDS model combining rough set (RS) theory and the Kolmogorov-Arnold Network (KAN). RS selects critical features without sacrificing detection accuracy, while KAN identifies patterns for precise classification. Performance metrics, including accuracy, precision, recall, false alarm, F1-score, and runtime, are derived from classification results. Ablation experiments using the CSE-CIC-IDS2018 and CIC-DDoS2019 datasets show that RK-IDS achieves accuracy of 95.20% and 96.88%, respectively. These results suggest RK-IDS offers better detection capabilities than traditional neural networks, making it highly applicable to modern ITS networks.