Anomaly Based Network Intrusion Detection System Using ML

Abstract

Network security has become one of the most critical aspects of modern computer systems. New cyber threats emerge every day, and many of them can evade traditional security mechanisms. Anomaly-based intrusion detection helps address this challenge by identifying unexpected or irregular behavior that could signal an attack. This project develops a machine-learning-driven intelligent detection system to spot anomalies in network traffic. By training models such as Random Forest and XGBoost on real-world datasets, the system learns to identify deviations from normal activity, including abnormal traffic volumes, access during unusual hours, or unexpected protocol usage. It focuses on effective feature extraction, handling imbalanced data, and supporting both binary and multiclass attack classification. The final system is designed to be scalable, interpretable, and dependable, enabling early detection of potential threats before they cause any damage.