Secure federated distillation GAN for CIDS in industrial CPS - Junwei LIANG; Geng YANG; Maode MA; Sadiq Muhammad (Tongxin xuebao)
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 designed, allowing multiple industrial CPS to collectively build a comprehensive intrusion detection system (IDS) to recognize 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 effective in detecting various types of attacks on industrial CPS.