Machine Learning-Based Intrusion Detection Methods in IoT Systems: A Comprehensive Review

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Brunel Rolack Kikissagbe, Meddi Adda
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引用次数: 0

Abstract

The rise of the Internet of Things (IoT) has transformed our daily lives by connecting objects to the Internet, thereby creating interactive, automated environments. However, this rapid expansion raises major security concerns, particularly regarding intrusion detection. Traditional intrusion detection systems (IDSs) are often ill-suited to the dynamic and varied networks characteristic of the IoT. Machine learning is emerging as a promising solution to these challenges, offering the intelligence and flexibility needed to counter complex and evolving threats. This comprehensive review explores different machine learning approaches for intrusion detection in IoT systems, covering supervised, unsupervised, and deep learning methods, as well as hybrid models. It assesses their effectiveness, limitations, and practical applications, highlighting the potential of machine learning to enhance the security of IoT systems. In addition, the study examines current industry issues and trends, highlighting the importance of ongoing research to keep pace with the rapidly evolving IoT security ecosystem.
物联网系统中基于机器学习的入侵检测方法:全面回顾
物联网(IoT)的兴起改变了我们的日常生活,它将物体连接到互联网,从而创造出交互式的自动化环境。然而,这种快速扩张引发了重大的安全问题,尤其是在入侵检测方面。传统的入侵检测系统(IDS)往往不适合物联网特有的动态和多样化网络。机器学习正在成为应对这些挑战的一种有前途的解决方案,它提供了应对复杂和不断发展的威胁所需的智能和灵活性。本综述探讨了物联网系统中用于入侵检测的不同机器学习方法,涵盖了有监督、无监督和深度学习方法以及混合模型。它评估了这些方法的有效性、局限性和实际应用,强调了机器学习在增强物联网系统安全性方面的潜力。此外,本研究还探讨了当前的行业问题和趋势,强调了持续研究对于跟上快速发展的物联网安全生态系统的重要性。
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
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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