Intelligent Hybrid Machine Learning Framework for Botnet Attack Detection in IoT Systems

Abstract

The Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment is an advanced cybersecurity framework designed to enhance the security of Internet of Things (IoT) networks against sophisticated botnet attacks. With the rapid growth of IoT devices in smart homes, healthcare, industrial automation, and smart cities, IoT ecosystems have become highly vulnerable to large-scale cyber threats. This model integrates multiple machine learning techniques—such as supervised and unsupervised learning algorithms—to accurately detect and classify malicious network traffic in real time. By combining feature selection, anomaly detection, and ensemble learning approaches, the hybrid system improves detection accuracy while reducing false positives and computational overhead. The proposed model leverages network traffic data, behavioral patterns, and real-time monitoring to identify botnet activities such as Distributed Denial of Service (DDoS), data exfiltration, and unauthorized access. Additionally, it incorporates adaptive learning mechanisms to handle evolving attack patterns and zero-day threats. The system provides automated alerts, threat intelligence insights, and scalable deployment within IoT infrastructures.