IoT Cybersecurity: On the Use of Boosting-Based Approaches for Botnet Detection

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.