IOT Botnet Detection using Machine Learning

The rapid expansion of interconnected smart devices has enabled widespread automation and connectivity across smart homes, healthcare, manufacturing, and industrial control systems. However, this large-scale connectivity has significantly increased vulnerabilities, leading to a sharp rise in IoT botnet attacks. Compromised devices are remotely controlled and used to perform cyber-crimes such as Distributed Denial of Ser- vice (DDoS), unauthorized data access, and aggressive network flooding. Traditional signature-based detection methods fail to identify evolving botnet families due to rapid mutation of attack patterns. This paper introduces a machine learning-based intelligent detection framework capable of identifying IoT botnet behavior through traffic flow analysis. The proposed system preprocesses collected network traffic, extracts relevant features, classifies flows as benign (0) or malicious (1), and triggers an automated alert notification when malicious activity is detected. Experimental evaluation demonstrates highly reliable perfor- mance and strong prediction accuracy, proving the suitability of machine learning methods for real-time IoT botnet detection and prevention. Additional discussions include feature selection techniques, model optimization, and deployment considerations for scalable and robust IoT security solutions.