关键词: 车联网, 入侵检测, 零日攻击, Transformer, 自适应模糊神经网络推理系统（ANFIS）

Abstract: Existing machine learning methods suffer from over-reliance on sample data and insensitivity to anomalous data when confronted with zero-day attack detection, thus making it difficult for intrusion detection systems to effectively defend against zero-day attacks. Therefore, a hybrid vehicle network intrusion detection system based on Transformer and ANFIS (Adaptive-Network-based Fuzzy Inference Systems) was proposed. Firstly, a data enhancement algorithm was designed and the problem of unbalanced data samples was solved by denoising first and then generating. Secondly, a feature engineering module was designed by introducing non-linear feature interactions into complex feature combinations. Finally, the self-attention mechanism of Transformer and the adaptive learning method of ANFIS were combined, which enhanced the ability of feature representation and reduced the dependence on sample data. The proposed system was compared with other SOTA (State Of The Arts) algorithms on CICIDS-2017 and UNSW-NB15 intrusion datasets. Experimental results show that for zero-day attacks, the proposed system achieves 98.64% detection accuracy and 98.31% F1 value on CICIDS-2017 intrusion dataset, and 93.07% detection accuracy and 92.43% F1 value on UNSW-NB15 intrusion dataset, which validates the proposed algorithm's high accuracy and strong generalisation ability for zero-day attack detection.

Key words: internet of vehicles, intrusion detection, zero-day attack, Transformer, Adaptive-Network-based Fuzzy Inference System (ANFIS)