Honeypot technologies provide an effective defense strategy for the Industrial Internet of Things (IIoT), particularly in enhancing the Advanced Metering Infrastructure's (AMI) security by bolstering the network intrusion detection system. For this security paradigm to be fully realized, it necessitates the active participation of small-scale power suppliers (SPSs) in implementing honeypots and engaging in collaborative data sharing with traditional power retailers (TPRs). To motivate this interaction, TPRs incentivize data sharing with tangible rewards. However, without access to an SPS's confidential data, it is daunting for TPRs to validate shared data, thereby risking SPSs' privacy and increasing sharing costs due to voluminous honeypot logs. These challenges can be resolved by utilizing Federated Learning (FL), a distributed machine learning (ML) technique that allows for model training without data relocation. However, the conventional FL algorithm lacks the requisite functionality for both the security defense model and the rewards system of the AMI network. This work presents two solutions: first, an enhanced and cost-efficient FedAvg algorithm incorporating a novel data quality measure, and second, FedPot, the development of an effective security model with a fair incentives mechanism under an FL architecture. Accordingly, SPSs are limited to sharing the ML model they learn after efficiently measuring their local data quality, whereas TPRs can verify the participants' uploaded models and fairly compensate each participant for their contributions through rewards. Simulation results, drawn from realistic mircorgrid network log datasets, demonstrate that the proposed solutions outperform state-of-the-art techniques by enhancing the security model and guaranteeing fair reward distributions.

翻译：蜜罐技术为工业物联网（IIoT）提供了一种有效的防御策略，尤其通过增强网络入侵检测系统来提升高级计量基础设施（AMI）的安全性。为实现这一安全范式，需要小型电力供应商（SPSs）积极参与部署蜜罐，并与传统电力零售商（TPRs）进行协作数据共享。为激励这种互动，TPRs通过实物奖励鼓励数据共享。然而，在无法访问SPS机密数据的情况下，TPRs难以验证共享数据的真实性，这不仅危及SPS的隐私，也因海量蜜罐日志而增加了共享成本。这些挑战可通过利用联邦学习（FL）来解决，这是一种分布式机器学习（ML）技术，允许在不迁移数据的情况下进行模型训练。然而，传统的FL算法缺乏对AMI网络安全防御模型和奖励系统的必要功能支持。本文提出了两种解决方案：首先，一种融合了新型数据质量度量的增强型高性价比FedAvg算法；其次，在FL架构下开发了具有公平激励机制的有效安全模型FedPot。据此，SPS仅需在高效评估其本地数据质量后共享其学习到的ML模型，而TPRs则可验证参与者上传的模型，并通过奖励机制公平补偿每位参与者的贡献。基于真实微电网网络日志数据集的仿真结果表明，所提方案通过增强安全模型和保证奖励分配的公平性，性能优于现有先进技术。