Towards Interpretable and Lightweight Intrusion Detection for In-vehicle Network
2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE)(2022)
摘要
Along with the improvement of informatization and intelligence in modern vehicles, the attacks aimed at in-vehicle network has been becoming an emerging threat. Current methods such as neural network-based methods have shown a state-of-the-art performance on intrusion detection based on in-vehicle network monitoring. However, the limitations in interpretability and resource consumption remain the non-negligible obstales for practical applications and deployments. In this paper, we propose and build an interpretable and lightweight framework based on shapelets model for in-vehicle network intrusion detection. We conduct massive experiments on a real-world dataset, namely Car-Hacking 2019 to evaluate our detection framework. Experiments demonstrate that our proposed method can reach or surpass state-of-the-art methods in four different adversarial scaneraios with extremely low time consumption, while providing interpretable results with visual explanations for further investigations.
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关键词
car hacking,interpretability,network intrusion detection,in-vehicle network
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