通信学报2023，Vol.44Issue(12)：230-244,15.DOI:10.11959/j.issn.1000-436x.2023216
基于安全联邦蒸馏GAN的工业CPS协作入侵检测系统
Secure federated distillation GAN for CIDS in industrial CPS
摘要
Abstract
Aiming at the data island problem caused by the imperativeness of confidentiality of sensitive information,a secure and collaborative intrusion detection system(CIDS)for industrial cyber physical systems(CPS)was proposed,called PFD-GAN.Specifically,a novel semi-supervised intrusion detection model was firstly developed by improving external classifier-generative adversarial network(EC-GAN)with Wasserstein distance and label condition,to strengthen the classification performance through the use of synthetic data.Furthermore,local differential privacy(LDP)technology was incorporated into the training process of developed EC-GAN to prevent sensitive information leakage and ensure privacy and security in collaboration.Moreover,a decentralized federated distillation(DFD)-based collaboration was de-signed,allowing multiple industrial CPS to collectively build a comprehensive intrusion detection system(IDS)to recog-nize the threats under the entire cyber systems without sharing a uniform template model.Experimental evaluation and theory analysis demonstrate that the proposed PFD-GAN is secure from the threats of privacy leaking and highly effec-tive in detecting various types of attacks on industrial CPS.关键词
入侵检测系统/信息物理系统/生成对抗网络/本地差分隐私/去中心化联邦蒸馏Key words
intrusion detection system/cyber physical system/generative adversarial network/local differential privacy/decentralized federated distillation分类
信息技术与安全科学引用本文复制引用
梁俊威,杨耿,马懋德,Muhammad Sadiq..基于安全联邦蒸馏GAN的工业CPS协作入侵检测系统[J].通信学报,2023,44(12):230-244,15.基金项目
广东省青年创新人才基金资助项目(No.2022KQNCX233) （No.2022KQNCX233）
公共大数据国家重点实验室基金资助项目(No.PBD2022-14) （No.PBD2022-14）
深圳市自然科学基金资助项目(No.20220820003203001)The Guangdong Provincial Research Platform and Project(No.2022KQNCX233),The Foundation of State Key Laboratory of Public Big Data(No.PBD2022-14),The Shenzhen Natural Science Foundation(No.20220820003203001) （No.20220820003203001）
