A lightweight approach for network intrusion detection in industrial cyber-physical systems based on knowledge distillation and deep metric learning

作者：

Highlights：

• The resource-constrained devices in Cyber-Physical Systems are considered.

• The KD-TCNN model is utilized with knowledge distillation and metric learning.

• A neural network training method called K-fold cross training is proposed.

• The proposed model is tested using benchmark intrusion detection datasets.

• Proposed mothed outperforms many state-of-the-art models.

摘要

•The resource-constrained devices in Cyber-Physical Systems are considered.•The KD-TCNN model is utilized with knowledge distillation and metric learning.•A neural network training method called K-fold cross training is proposed.•The proposed model is tested using benchmark intrusion detection datasets.•Proposed mothed outperforms many state-of-the-art models.

论文关键词：Intrusion detection,Industrial cyber-physical system,Knowledge distillation,Triplet neural network

论文评审过程：Received 6 February 2022, Revised 16 May 2022, Accepted 27 May 2022, Available online 2 June 2022, Version of Record 17 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117671