【Author】
Cui, Lei; Qu, Youyang; Xie, Gang; Zeng, Deze; Li, Ruidong; Shen, Shigen; Yu, Shui
【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
【Abstract】Internet of Things (IoT) anomaly detection is significant due to its fundamental roles of securing modern critical infrastructures, such as falsified data injection detection and transmission line faults diagnostic in smart grids. Researchers have proposed various detection methods fostered by machine learning (ML) techniques. Federated learning (FL), as a promising distributed ML paradigm, has been employed recently to improve detection performance due to its advantages of privacy-preserving and lower latency. However, existing FL-based methods still suffer from efficiency, robustness, and security challenges. To address these problems, in this article, we initially introduce a blockchain-empowered decentralized and asynchronous FL framework for anomaly detection in IoT systems, which ensures data integrity and prevents single-point failure while improving the efficiency. Further, we design an improved differentially private FL based on generative adversarial nets, aiming to optimize data utility throughout the training process. To the best of our knowledge, it is the first system to employ a decentralized FL approach with privacy-preserving for IoT anomaly detection. Simulation results on the real-world dataset demonstrate the superior performance from aspects of robustness, accuracy, and fast convergence while maintaining high level of privacy and security protection.
【Keywords】Anomaly detection; Servers; Internet of Things; Blockchains; Privacy; Collaborative work; Security; Asynchronous federated learning; differential privacy protection; IoT anomaly detection; security
【标题】用于物联网基础设施异常检测的安全和隐私增强联邦学习
【摘要】物联网 (IoT) 异常检测具有重要意义，因为它在保护现代关键基础设施方面发挥着重要作用，例如智能电网中的伪造数据注入检测和传输线故障诊断。研究人员提出了由机器学习 (ML) 技术培育的各种检测方法。联邦学习 (FL) 作为一种有前途的分布式机器学习范式，由于其隐私保护和低延迟的优势，最近已被用于提高检测性能。然而，现有的基于 FL 的方法仍然面临着效率、鲁棒性和安全性方面的挑战。为了解决这些问题，在本文中，我们首先介绍了一种基于区块链的去中心化异步 FL 框架，用于物联网系统中的异常检测，在提高效率的同时确保数据完整性并防止单点故障。此外，我们设计了一种基于生成对抗网络的改进的差分私有 FL，旨在优化整个训练过程中的数据效用。据我们所知，它是第一个采用去中心化 FL 方法和隐私保护的物联网异常检测系统。在真实世界数据集上的模拟结果从鲁棒性、准确性和快速收敛等方面展示了卓越的性能，同时保持了高水平的隐私和安全保护。
【关键词】异常检测；服务器；物联网;区块链；隐私;协作工作；安全;异步联邦学习；差异化隐私保护；物联网异常检测；安全
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