Integrated BERT and Transfer Learning for Adaptive Intrusion Detection in Vehicle CAN Network
In order to improve the communication security of distributed on-board Electronic Control Unit(ECU),an unsupervised adaptive intrusion detection system based on BERT and transfer learning is proposed.The natural language model BERT is used to learn the identifier sequence features in the Controller Area Network(CAN)bus,and the mask language model is used as the training target for unsupervised anomaly detection.Combined with transfer learning,the data training of different vehicle models is completed.Experimental results on large-scale CAN datasets show that the detection accuracy of the detection system for Denial of Service(DoS),fuzzy and spoofing attacks reaches 100%,99.99%and 99.97%respectively,and the adaptive detection of new vehicle models can be realized through model fine-tuning.
Vehicle networkController Area Network(CAN)Electronic Control Unit(ECU)Intrusion detection systemTransfer learning
NETL
NSTL
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