Ensemble-Based Botnet Attack Detection and Classification Using Machine Learning Algorithms on NBaIoT Dataset
Mamta Rawat, Avneet Singh Bedi, Balvinder Singh, Sneha Gupta, Gaurav Singal, Preeti Kaur · 2024
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.