A Hybrid Machine Learning Framework for Preventing and Detecting IoT Botnet Threats

Abstract

The exponential growth of IoT devices has introduced significant cybersecurity challenges, particularly from botnet attacks that exploit their limited security features. This project proposes a dual-pronged machine learning approach to both prevent and detect IoT-based botnet threats. The prevention module uses lightweight ML models to monitor and identify anomalies in real-time at the device level, while the detection module leverages advanced deep learning techniques to analyze traffic patterns and recognize zero-day attacks. The hybrid system combines edge-based rapid response with cloud-based analytical depth, ensuring scalability and low latency. By integrating behavioral analysis, real-time threat detection, and adaptive learning mechanisms, this strategy offers a robust, scalable, and efficient solution to enhance security in modern IoT ecosystems.