A Privacy-Preserving Federated Intrusion Detection Framework with Reputation-Aware Client Selection and Integrity Verification

<title>Abstract</title> <p>With the increasing number of Industrial Internet of Things (IIoT) networks, critical infrastructures are now more vulnerable to cyberattacks. In this context, the need for distributed and privacy-preserving intrusion detection systems has become essential. In this paper, we introduce a secure federated learning framework for intrusion detection in IIoT networks that supports model training in non-IID environments without sharing raw data. In this system, each client maintains a lightweight MLP model locally, and a client-level DP-SGD is used to enhance privacy and hashing to maintain update integrity. Also, to consciously select clients and reduce the impact of malicious clients, a reputation-based mechanism is proposed that leverages the ideas of trust management in blockchain, but can be implemented without the need for a full blockchain implementation. The performance of the proposed model on the Edge-IIoTset dataset in binary and multi-class classification and in 3, 5, and 7 clients shows that the proposed model achieves an accuracy of over 98% in all scenarios, which is close to the results of the centralized approach.</p>