IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?
Abstract
The Internet of things (IoT), which integrates a variety of devices into networks to provide advanced and intelligent services, has to protect user privacy and address attacks such as spoofing attacks, denial of service (DoS) attacks, jamming, and eavesdropping. We investigate the attack model for IoT systems and review the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL). ML-based IoT authentication, access control, secure offloading, and malware detection schemes to protect data privacy are the focus of this article. We also discuss the challenges that need to be addressed to implement these ML-based security schemes in practical IoT systems.
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Cha et al. designed a security audit system conforming to ISO/IEC 15408-2 with blockchain as the underlying architecture.
“CHA et al. [104] designed a security audit system that conforms to the ISO/IEC 15408-2 standard with blockchain as the underlying architecture.”
From A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends (2024) · cites this paper as [104]
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- Authors
- Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D.
- Affiliations
- Xiamen University (CN)
- Rutgers, The State University of New Jersey (US)
- New York University (US)
- Venue
- IEEE Signal Processing Magazine
- Published
- 2018-09
- Volume
- 35
- Issue
- 5
- Pages
- 41-49
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2892077825
- Cited by
- 681
- References
- 3
- Citations by year
- 2026: 31 · 2025: 67 · 2024: 81 · 2023: 135 · 2022: 126 · 2021: 112 · 2020: 82 · 2019: 43 · 2018: 4
- FWCI
- 37.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- Internet of Things security, machine learning for security, IoT attacks, spoofing attacks, denial-of-service attacks, jamming attacks, eavesdropping, IoT device authentication, access control, malware detection, data privacy, reinforcement learning, supervised learning, unsupervised learning
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