计算机科学
异常检测
稳健性(进化)
差别隐私
单点故障
异步通信
计算机安全
分布式计算
物联网
数据建模
块链
信息隐私
数据挖掘
计算机网络
数据库
基因
生物化学
化学
作者
Lei Cui,Youyang Qu,Gang Xie,Deze Zeng,Ruidong Li,Shigen Shen,Shui Yu
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-05-01
卷期号:18 (5): 3492-3500
被引量:59
标识
DOI:10.1109/tii.2021.3107783
摘要
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.
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