Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security

• URL: http://arxiv.org/abs/2410.01016v2
• Date: Sun, 6 Oct 2024 19:38:56 GMT
• Title: Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security
• Authors: Mona Esmaeili, Morteza Rahimi, Hadise Pishdast, Dorsa Farahmandazad, Matin Khajavi, Hadi Jabbari Saray,
• Abstract summary: Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. To efficiently secure IoT devices, real-time detection of intrusion systems is critical. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security.
• Score: 1.2369895513397127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a considerable vulnerability and threat to the privacy, security, functionality, and availability of critical systems, which leads to operational disruptions, financial losses, identity thefts, and data breaches. To efficiently secure IoT devices, real-time detection of intrusion systems is critical, especially those using machine learning to identify threats and mitigate risks and vulnerabilities. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security, concentrating on real-time responsiveness, detection accuracy, and algorithm efficiency. Key studies were reviewed from all well-known academic databases, and a taxonomy was provided for the existing approaches. This review also highlights existing research gaps and outlines the limitations of current IoT security frameworks to offer practical insights for future research directions and developments.

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