Analyzing and detecting Botnet Attacks using Anomaly Detection with Machine Learning

Botnet attacks have become a significant danger in the digital landscape, posing a significant risk to personnel and public. The ability to detect and mitigate botnet attacks is critical to maintaining the security and integrity of network infrastructure. This research study presents a comprehensive study on analyzing and detecting botnet attacks using Anomaly detection techniques with machine learning. The important objective of the research is botnet attack detection using Anomaly detection with machine learning. Anomaly detection is a fundamental approach that involves analyzing network traffic patterns and identifying deviations from normal behavior that may indicate the presence of botnet activity. The research explores various anomaly detection techniques, including statistical methods, machine learning algorithms, and network-based approaches, to accurately identify botnet-related anomalies. The proposed work involves a multi-layered approach to botnet attack detection. It includes data preprocessing, feature extraction, and model training. First, features such as traffic volume, packet flow, and communication patterns are extracted from network traffic data. machine learning classify are support vector machines, and random forests are trained on these features to develop accurate and robust botnet detection models.