Efficient Botnet Attack Detection in IOT Environments Using Hybrid Machine Learning Model - Mamatha; G. V.S Ananthanath (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
As technology becomes more integrated into our daily lives, the security of ongoing operating complex interconnected systems has become quite paramount. This is, therefore, one of the most astonishing threats: The botnet attacks are directed against vulnerable devices to cooperate together for some malicious purposes. This paper presents a concrete and highly scalable botnet activities detection in IoT environment with machine learning. The Random Forest algorithm has been adopted as it is considered robust and efficient with complex, high-dimensional data concerning network traffic. The detection model is successfully integrated into a user-friendly graphical web interface, which is developed using HTML, CSS, JavaScript, and with Flask used as a backend framework. This setup guarantees smooth interaction, efficient model deployment, and enables real-time analysis. This configuration emerges as a machine learning solution based on web-related technologies that facilitate the enhancement of IoT security and mitigation of related botnet threats.
Keywords: Botnet Detection; Hybrid Machine Learning; Random Forest; Internet of Things (IoT); Cybersecurity; Receiver; Operating Characteristic (ROC) Curve; Precision-Recall (PR) Curve