A privacy-enhanced secure federated intrusion detection method

Abstract:

Intrusion detection systems (IDS) face security challenges of generative model inversion attacks. And Federated GAN Attacks are the particularly characteristic data security threat to federated IDS. To improve data privacy in federated IDS, a universal privacy-enhanced federated intrusion detection (PEFID) method is proposed and is validated in diverse attack-defense simulation scenarios. PEFID jointly enhances data privacy at both the feature level and the model level. From the feature level, an improved adaptive privacy enhancing module is proposed to adaptively adjust the regularization degree of representation learning to balance privacy protection and task learning. Besides, controllable perturbations are injected into the hidden variables to further degrade the traceability of the gradient. From the model level, a label smoothing strategy combined with prediction confidence is proposed to deal with label inversion. Each client can individually adjust the soft label value according to the prediction confidence, assigning victim data a more lenient soft label value to mitigate the consistent attack. Experimental results on the CICIDS2018 and UNSW-NB15 datasets show that PEFID can effectively resist federated GAN attacks in various network scenarios. Compared with other methods, PEFID can better balance privacy and performance with controllable time complexity. It can still maintain superior defensive efficacy even in the case of single point penetration. The proposed method is both universal and lightweight, which can be adapted to existing federated IDS to significantly enhance data privacy with minimal performance cost.