Leveraging The Machine Learning (ML) Techniques For Enhancing The Intrusion Detection In Internet Of Things (IoT) Security

Amardeep Singh BH

Vol. 7, Issue 1, Jan-Dec 2021

Page Number: 272 - 283

Abstract:

The rapid proliferation of Internet of Things (IoT) devices has transformed various industries by enabling seamless connectivity and smart automation in domains such as healthcare, smart cities, industrial control, and home automation. However, this unprecedented growth introduces critical security challenges due to the resource-constrained nature of IoT devices, diverse protocols, and the heterogeneity of the network environment. Traditional security mechanisms and intrusion detection systems (IDS) often fall short in addressing these challenges effectively, particularly in detecting novel and sophisticated cyber-attacks. To overcome these limitations, machine learning (ML) techniques have gained significant attention for their ability to analyze large volumes of network and device data, learn complex behavioral patterns, and identify anomalies indicative of security breaches. This paper provides a comprehensive review of state-of-the-art ML approaches applied to IoT intrusion detection, covering supervised, unsupervised, and hybrid learning methods. It highlights their strengths, such as adaptability to evolving threats and capability to handle heterogeneous data, as well as their inherent challenges, including the scarcity of labeled data and the computational constraints of IoT environments. The discussion includes popular datasets, evaluation metrics, and deployment scenarios, emphasizing the importance of lightweight, scalable, and privacy-preserving IDS frameworks. Additionally, the paper explores emerging trends such as federated learning and edge-based detection to mitigate privacy and latency concerns. Finally, open research challenges and future directions are identified to inspire the development of more robust, efficient, and interpretable ML-driven intrusion detection solutions for securing the rapidly expanding IoT ecosystem.

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