MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network
<|reference_start|>MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network: Control Area Network (CAN) is an essential communication protocol that interacts between Electronic Control Units (ECUs) in the vehicular network. However, CAN is facing stringent security challenges due to innate security risks. Intrusion detection systems (IDSs) are a crucial safety component in remediating Vehicular Electronics and Systems vulnerabilities. However, existing IDSs fail to identify complexity attacks and have higher false alarms owing to capability bottleneck. In this paper, we propose a self-supervised multi-knowledge fused anomaly detection model, called MKF-ADS. Specifically, the method designs an integration framework, including spatial-temporal correlation with an attention mechanism (STcAM) module and patch sparse-transformer module (PatchST). The STcAM with fine-pruning uses one-dimensional convolution (Conv1D) to extract spatial features and subsequently utilizes the Bidirectional Long Short Term Memory (Bi-LSTM) to extract the temporal features, where the attention mechanism will focus on the important time steps. Meanwhile, the PatchST captures the combined contextual features from independent univariate time series. Finally, the proposed method is based on knowledge distillation to STcAM as a student model for learning intrinsic knowledge and cross the ability to mimic PatchST. We conduct extensive experiments on six simulation attack scenarios across various CAN IDs and time steps, and two real attack scenarios, which present a competitive prediction and detection performance. Compared with the baseline in the same paradigm, the error rate and FAR are 2.62\% and 2.41\% and achieve a promising F1-score of 97.3\%.<|reference_end|>
@article{cheng2024mkf-ads:,
    title={MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection
  System for Control Area Network},
    author={Pengzhou Cheng, Zongru Wu, and Gongshen Liu},
    journal={arXiv preprint arXiv:2403.04293},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04293},
    primaryClass={cs.AI cs.CR}
}