A Comparative Study of Using Boosting-Based Machine Learning Algorithms for IoT Network Intrusion Detection

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Abstract The Internet-of-Things (IoT) environment has revolutionized the quality of living standards by enabling seamless connectivity and automation. However, the widespread adoption of IoT has also brought forth significant security challenges for manufacturers and consumers alike. Detecting network intrusions in IoT networks using machine learning techniques shows promising potential. However, selecting an appropriate machine learning algorithm for intrusion detection poses a considerable challenge. Improper algorithm selection can lead to reduced detection accuracy, increased risk of network infection, and compromised network security. This article provides a comparative evaluation to six state-of-the-art boosting-based algorithms for detecting intrusions in IoT. The methodology overview involves benchmarking the performance of the selected boosting-based algorithms in multi-class classification. The evaluation includes a comprehensive classification performance analysis includes accuracy, precision, detection rate, F1 score, as well as a temporal performance analysis includes training and testing times.

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