FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems

• URL: http://arxiv.org/abs/2210.04052v3
• Date: Wed, 2 Aug 2023 07:36:09 GMT
• Title: FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems
• Authors: Jiahui Chen, Yi Zhao, Qi Li, Xuewei Feng, Ke Xu
• Abstract summary: We propose two privacy evaluation metrics designed for FL-based NIDSs. We propose FedDef, a novel optimization-based input perturbation defense strategy with theoretical guarantee. We experimentally evaluate four existing defenses on four datasets and show that our defense outperforms all the baselines in terms of privacy protection.
• Score: 15.39058389031301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-based methods, federated learning (FL) allows multiple users to train a global model on the basis of respecting individual data privacy. However, it has not yet been systematically evaluated how robust FL-based NIDSs are against existing privacy attacks under existing defenses. To address this issue, we propose two privacy evaluation metrics designed for FL-based NIDSs, including (1) privacy score that evaluates the similarity between the original and recovered traffic features using reconstruction attacks, and (2) evasion rate against NIDSs using adversarial attack with the recovered traffic. We conduct experiments to illustrate that existing defenses provide little protection and the corresponding adversarial traffic can even evade the SOTA NIDS Kitsune. To defend against such attacks and build a more robust FL-based NIDS, we further propose FedDef, a novel optimization-based input perturbation defense strategy with theoretical guarantee. It achieves both high utility by minimizing the gradient distance and strong privacy protection by maximizing the input distance. We experimentally evaluate four existing defenses on four datasets and show that our defense outperforms all the baselines in terms of privacy protection with up to 7 times higher privacy score, while maintaining model accuracy loss within 3% under optimal parameter combination.

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