1 Department of Software Engineering. University of Hertfordshire, United Kingdom.
2 Department of Computing, University of Dundee, Dundee, United Kingdom.
3 Department of Data Analysis, University of Nottingham, United Kingdom.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(02), 1116-1138
Article DOI: 10.30574/wjarr.2024.24.2.3449
Received on 29 September 2024; revised on 09 November 2024; accepted on 11 November 2024
The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.
IoT Security; Lightweight Machine Learning; Intrusion Detection; Anomaly Detection; Edge Computing; Federated Learning
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Sofiritari Ibikoroma Amgbara, Chukwuebuka Akwiwu-Uzoma and Ola David. Exploring lightweight machine learning models for personal internet of things (IOT) device security. World Journal of Advanced Research and Reviews, 2024, 24(2), 1116-1138. Article DOI: https://doi.org/10.30574/wjarr.2024.24.2.3449