Privacy-Preserving Honeypot-Based Detector in Smart Grid Networks: A New Design for Quality-Assurance and Fair Incentives Federated Learning Framework

Adopting honeypot defenses is a promising technology for protecting the industrial Internet of Things (IIoT), particularly the Advanced Metering Infrastructure (AMI). The effectiveness of AMI defense is entirely reliant on the deployment of honeypots by small-scale power suppliers (SPSs) and then sharing the defense data with traditional power retailers (TPRs) to build anomaly detectors. TPR encourages the SPSs to share their collected honeypot logs by designing proper rewards. However, TPRs cannot confirm the validity of the shared defense data unless they have access to SPSs' private data, compromising their privacy since SPSs may be reluctant to disclose their private collected data. In addition, the honeypot logs are large, which increases the sharing costs. Federated Learning (FL), as a promising privacy-preserving machine learning technique, can solve these problems. Yet, the conventional FL algorithm cannot optimally fit the security defense model and the associated returned rewards in the AMI network. Thus, using two proposed solutions, including a modified FedAvg algorithm, this paper proposes a privacy-preserving and cost-effective FL framework for efficient security model development and fair rewards, in which SPSs can share only the learned ML model while TPR can validate the quality of the uploaded models and compensate all participants with appropriate rewards that reflect their contributions. The proposed framework also considers malicious participants who claim high-quality data while sharing bad models. We run extensive simulations on realistic log datasets, and the results show that the proposed solutions outperform existing approaches.