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.

The Internet of Things (IoT) is receiving increasing attention from academia and industry. However, improving the security of the IoT environment is critical for fostering trust in it and contributing to its growth in the manufacturing market. This study comparatively analyzes current methods for detecting intruders and malicious activities in IoT networks by introducing tree-based machine learning algorithms. It presents a research gap analysis of the current literature. Furthermore, an empirical evaluation study is presented to explore the potential of tree-based approaches to detect intruders in IoT networks. It compares the performance of bagging and boosting techniques in botnet detection by conducting an extensive experimental benchmarking.

Botnets are a major security threat in the Internet of Things (IoT), posing significant risks to user privacy, network availability, and the integrity of IoT devices. With the increasing availability of large datasets that contain hundreds or even thousands of variables, selecting the right set of features can be a challenging task. Feature selection is a critical step in developing effective machine learning-based botnet detection systems, as it enables the selection of a subset of features that are most relevant for detection. This paper provides a comprehensive review of filtering based feature selection techniques for botnet detection in IoT. It examines a range of filtering based techniques and evaluates their effectiveness in addressing the challenges and limitations of botnet detection in IoT. It aims to identify the gaps in the literature and areas for future research, and discuss the broader implications of findings for the field of IoT botnet detection. This review provides valuable insights and guidance for researchers and practitioners working on botnet detection in IoT, and highlights the importance of effective feature selection in developing robust and reliable detection systems.

The rapid expansion of Internet of Things (IoT) adoption has brought about significant cybersecurity challenges, with botnet attacks being a critical concern. To address this issue, machine learning algorithms, particularly boosting-based approaches, have shown promise in detecting and mitigating botnet intrusions. However, the selection of an appropriate algorithm plays a crucial role in achieving accurate detection and reducing the probability of infection. This article focuses on the utilization of boosting-based algorithms for botnet detection in IoT environments. It evaluates the performance of five boosting-based machine learning algorithms in botnet binary detection. The empirical findings underscored the significant potential of boosting-based algorithms in effectively detecting botnet attacks within IoT environments. The histogram gradient boosting algorithm achieved the best performance for binary detection with an accuracy rate of 0.999977. In addition, a temporal evaluation is presented to evaluate the computational requirements of each algorithm to cope with the resources constrained nature of IoT.

Moemen Ebrahim, Shawkat Guirguis, Christine Basta. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop). 2025.