A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks

Future Internet Pub Date : 2024-01-19 DOI:10.3390/fi16010032
Hassan Khazane, Mohammed Ridouani, Fatima Salahdine, N. Kaabouch
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Abstract

With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks.
全面评述物联网网络中的机器学习对抗性攻击
随着机器学习(ML)在各个应用领域的快速发展和显著成就,它已成为物联网(IoT)生态系统中的一个重要元素。这些用例中包括物联网安全,其中部署了许多系统来识别或挫败攻击,包括入侵检测系统 (IDS)、恶意软件检测系统 (MDS) 和设备识别系统 (DIS)。基于机器学习(ML)的物联网安全系统可以实现多个安全目标,包括检测攻击、在用户访问系统前对其进行身份验证以及对可疑活动进行分类。然而,ML 也面临着许多挑战,例如,为误导分类器而精心设计的对抗性攻击的出现。本文全面回顾了有关对抗性攻击和防御机制的知识体系,并特别关注三个著名的物联网安全系统:IDS、MDS 和 DIS。本文首先对物联网背景下的对抗性攻击进行了分类。然后,在一个二维框架内描述了生成对抗性攻击所采用的各种方法,并对其进行了分类。此外,我们还介绍了增强物联网安全性、抵御对抗性攻击的现有对策。最后,我们探讨了三种基于 ML 的物联网安全系统在对抗性攻击面前的脆弱性的最新文献。
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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