Ensemble-Based Botnet Attack Detection and Classification Using Machine Learning Algorithms on NBaIoT Dataset

Through botnet assaults, Mirai and BASHLITE present serious risks in the context of the Internet of Things (loT). A reliable detection and classification model is the need of the hour. This article presents a solution to the problem by suggesting an ensemble-based algorithm to detect and classify different categories of Mirai and BASHLITE attacks. We have used the NBaloT, a heterogeneous dataset, that purposefully com-promised on many devices, and exposes subtle attack categories. Interestingly, certain devices resist Mirai infections, improving our analysis. By utilizing important data types and various time-based window sizes, our approach seeks to create an effective model for identifying and classifying IoT botnet assaults to strengthen the IoT ecosystem against new attacks.