Ensemble Learning Based Detection of Mirai Botnet Attacks using Simulated, Generative AI and Hybrid Network Traffic

The Mirai botnet is one of the most damaging malware families targeting Internet of Things (IoT) devices. By manipulating weak login ID’s and unpatched vulnerabilities, Mirai converts compromised devices into large scale botnets accomplished to launch distributed denial-of-service attacks. This paper presents a complete study & evaluation of multiple machine learning classifiers such as Random Forest, K-Nearest Neighbors (KNN), Naïve Bayes and XGBoost for detecting Mirai botnet traffic. The models are assessed on three datasets namely real Mirai traffic, Generative AI based synthetic traffic, and a hybrid dataset combining both. Performance is measured using accuracy, precision, recall, and F1-score. The results determine that ensemble models such as Random Forest and XGBoost reliably achieve superior performance, mainly when trained on the hybrid dataset, making them extremely appropriate for real world IoT botnet detection systems.