【Author】
Ferrag, Mohamed Amine; Friha, Othmane; Maglaras, Leandros; Janicke, Helge; Shu, Lei
【Abstract】In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.
【Keywords】Internet of Things; Collaborative work; Data models; Servers; Computer crime; Deep learning; Training; Federated learning; intrusion detection; deep learning; cyber security; the IoT; blockchain
【标题】物联网网络安全的联邦深度学习：概念、应用和实验分析
【摘要】在本文中，我们提出了一项综合研究，并对物联网 (IoT) 应用程序中的网络安全联合深度学习方法进行了实验分析。具体来说，我们首先回顾了基于联邦学习的安全和隐私系统，适用于几种类型的物联网应用，包括工业物联网、边缘计算、无人机互联网、医疗保健物联网、车联网等。讨论了将联邦学习与区块链和恶意软件/入侵检测系统一起用于物联网应用程序。然后，我们回顾了基于联邦学习的安全和隐私系统中的漏洞。最后，我们使用三种深度学习方法，即循环神经网络 (RNN)、卷积神经网络 (CNN) 和深度神经网络 (DNN)，对联邦深度学习进行了实验分析。对于每个深度学习模型，我们研究了三个新的真实物联网流量数据集（即 Bot-IoT 数据集、MQTTset 数据集和 TON_IoT 数据集）下的集中式和联合式学习的性能。本文的目的是提供有关采用新兴网络安全技术的联合深度学习方法的重要信息。此外，它表明联合深度学习方法在确保物联网设备数据的隐私性和提供更高的检测攻击准确性方面优于机器学习的经典/集中式版本（非联邦学习）。
【关键词】物联网;协作工作；数据模型；服务器；计算机犯罪；深度学习；训练;联邦学习；入侵检测;深度学习；网络安全;物联网；区块链
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