ML-Driven Lightweight Botnet Detection System for IoT-Networks

Ashfaq Hussain Farooqi ()
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Ashfaq Hussain Farooqi: Department of Computer Science, Air University Islamabad, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6 Special Issue: 7, issue 7, 194-206

Abstract: The integration of cloud computing with the Internet of Things (IoT) seeks to create seamless connections between humans and devices, enhancing applications in areas like smart healthcare and home automation. However, this also brings significant security challenges. Our study addresses the critical need for an efficient anomaly detection system specifically designed for IoT-enabled cloud computing environments, a gap not previously explored at this scale. Utilizing the IoT-23 dataset, we evaluated various feature selection techniques in conjunction with classification algorithms to develop a lightweight anomaly detection model. Our results demonstrate that the decision tree classifier, paired with the correlation coefficient method for feature selection, achieved an impressive 99.98% accuracy rate, with an average processing time of just 5.2 seconds. This combination proved to be the most effective for real-time anomaly detection, presenting a promising approach for ensuring robust security in IoT networks as connectivity continues to grow.

Keywords: Internet of Things (IoT); Intrusion Detection System (IDS); Machine Learning (ML); Feature Selection Algorithm; Botnet Detection (search for similar items in EconPapers)
Date: 2024
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