A Comprehensive Review of Deep Learning Techniques for Anomaly Detection in IoT Networks: Methods, Challenges, and Datasets

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Roya Morshedi, S. Mojtaba Matinkhah
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引用次数: 0

Abstract

With the rapid growth of the Internet of Things (IoT) and the widespread deployment of smart connected devices, ensuring the security of these networks has become a critical challenge. Anomaly detection is considered one of the most effective techniques for identifying abnormal behaviors and cyber-attacks in IoT networks. In recent years, deep learning techniques have gained significant attention in this domain due to their powerful capabilities in automatic feature extraction and modeling complex patterns. This review article provides a comprehensive overview of deep learning methods applied to anomaly detection in IoT networks. Various deep architectures including CNNs, LSTMs, autoencoders, GANs, and hybrid models are analyzed and compared. In addition, commonly used datasets such as CICIDS2017, BoT-IoT, NSL-KDD, and TON_IoT are introduced and evaluated in terms of their quality and suitability for deep learning-based models. Key challenges including the lack of real-world data, high resource consumption, vulnerability to adversarial attacks, and lack of interpretability are also discussed. Finally, potential future research directions are suggested to enhance the performance and real-world applicability of deep learning-based anomaly detection systems in IoT environments.

Abstract Image

物联网网络异常检测的深度学习技术综述:方法、挑战和数据集
随着物联网(IoT)的快速发展和智能连接设备的广泛部署,确保这些网络的安全已成为一项关键挑战。异常检测被认为是识别物联网网络中异常行为和网络攻击的最有效技术之一。近年来,深度学习技术因其在自动特征提取和复杂模式建模方面的强大能力而在该领域受到了极大的关注。这篇综述文章提供了应用于物联网网络异常检测的深度学习方法的全面概述。分析和比较了cnn、lstm、自动编码器、gan和混合模型等各种深度架构。此外,介绍了CICIDS2017、BoT-IoT、NSL-KDD和TON_IoT等常用数据集,并对其质量和对深度学习模型的适用性进行了评估。主要挑战包括缺乏真实世界的数据、高资源消耗、易受对抗性攻击和缺乏可解释性。最后,提出了未来潜在的研究方向,以提高基于深度学习的异常检测系统在物联网环境中的性能和实际适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.10
自引率
0.00%
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0
审稿时长
19 weeks
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