FedPot：一种面向智能电网的质量感知协同激励蜜罐检测器
FedPot: A Quality-Aware Collaborative and Incentivized Honeypot-Based Detector for Smart Grid Networks
摘要：
蜜罐技术为工业物联网（IIoT）提供了一种有效的防御策略，特别是通过支持网络入侵检测系统来增强高级计量基础设施（AMI）的安全性。为了充分实现这种安全模式，需要小规模电力供应商（SPS）积极参与实施蜜罐，并与传统电力零售商（TPR）进行协作数据共享。为了激励这种互动，TPR用有形的奖励来激励数据共享。然而，在无法访问SPS的机密数据的情况下，TPR验证共享数据是令人生畏的，从而由于大量的蜜罐日志而使SPS的隐私面临风险并增加共享成本。这些挑战可以通过利用联邦学习（FL）来解决，这是一种分布式机器学习（ML）技术，允许在不重新定位数据的情况下进行模型训练。然而，传统的FL算法缺乏必要的功能，为AMI网络的安全防御模型和奖励系统。这项工作提出了两种解决方案：第一，增强和具有成本效益的FedAvg算法，结合了一种新的数据质量措施，第二，FedPot，一个有效的安全模型的发展与公平的激励机制下FL架构。因此，SPS仅限于在有效测量其本地数据质量后共享他们学习的ML模型，而TPR可以验证参与者上传的模型，并通过奖励公平地补偿每个参与者的贡献。仿真结果表明，从现实的microcorgrid网络日志数据集，所提出的解决方案优于国家的最先进的技术，增强了安全模型，并保证公平的奖励分配。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
