AI Based Network Intrusion Detection System

Abstract

A Network Intrusion Detection System (NIDS) using machine learning is designed to automatically identify malicious activities in network traffic and support real-time cyber-defence. In modern organizations, traditional signature-based systems and firewalls are no longer sufficient because attackers continuously generate new variants and zero-day exploits that do not match any known pattern, leading to undetected breaches and high financial, operational, and reputational damage. The proposed system addresses this gap by building an end-to-end pipeline that captures raw packets or flow records, performs systematic data preprocessing, selects the most informative features and then trains supervised learning models, such as Random Forest and Support Vector Machine, to distinguish normal behaviour from multiple attack categories including DoS, Probe, Remote-to-Local and User-to-Root. The dataset is cleaned, encoded and normalized to handle missing values, categorical attributes and scale differences, which significantly improves model robustness and reduces training time. Feature selection techniques are applied to remove redundant attributes, enhance generalization, and achieve faster prediction latency that is suitable for online deployment. The trained model is integrated with a lightweight backend service that receives live network statistics, invokes the classifier, and generates alerts with severity levels whenever suspicious activity is detected. Security analysts can then use a web dashboard to monitor alerts, inspect detailed connection logs, confirm or dismiss incidents and export reports for compliance or forensic analysis.