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Advancing CAN Network Security through RBM-Based Synthetic Attack Data Generation for Intrusion Detection Systems
Huacheng Li, Jingyong Su, Kai Wang
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The rapid development of network technologies and industrial intelligence has augmented the connectivity and intelligence within the automotive industry. Notably, in the Internet of Vehicles (IoV), the Controller Area Network (CAN), which is crucial for the communication of electronic control units but lacks inbuilt security measures, has become extremely vulnerable to severe cybersecurity threats. Meanwhile, the efficacy of Intrusion Detection Systems (IDS) is hampered by the scarcity of sufficient attack data for robust model training. To overcome this limitation, we introduce a novel methodology leveraging the Restricted Boltzmann Machine (RBM) to generate synthetic CAN attack data, thereby producing training datasets with a more balanced sample distribution. Specifically, we design a CAN Data Processing Module for transforming raw CAN data into an RBM-trainable format, and a Negative Sample Generation Module to generate data reflecting the distribution of CAN data frames denoting network intrusions. Experimental results show the generated data significantly improves IDS performance, with CANet accuracy rising from 0.6477 to 0.9725 and EfficientNet from 0.1067 to 0.1555. Code is available at https://github.com/wangkai-tech23/CANDataSynthetic.
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