计算机科学
物联网
入侵检测系统
计算机安全
信息隐私
互联网隐私
作者
Pedro Ruzafa-Alcazar,Pablo Fernández Saura,Enrique Marmol-Campos,Aurora González-Vidal,José L. Hernández-Ramos,Jorge Bernal Bernabé,Antonio Skármeta
标识
DOI:10.1109/tii.2021.3126728
摘要
Federated learning (FL) has attracted significant interest given its prominent advantages and applicability in many scenarios. However, it has been demonstrated that sharing updated gradients/weights during the training process can lead to privacy concerns. In the context of the Internet of Things (IoT), this can be exacerbated due to intrusion detection systems (IDSs), which are intended to detect security attacks by analyzing the devices' network traffic. Our work provides a comprehensive evaluation of differential privacy techniques, which are applied during the training of an FL-enabled IDS for industrial IoT. Unlike previous approaches, we deal with nonindependent and identically distributed data over the recent ToN_IoT dataset, and compare the accuracy obtained considering different privacy requirements and aggregation functions, namely FedAvg and the recently proposed Fed+. According to our evaluation, the use of Fed+ in our setting provides similar results even when noise is included in the federated training process.
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