AI-Based Network Intrusion Detection System

Abstract

The increasing use of computer networks, cloud platforms, and connected digital services has created a growing need for intelligent and adaptive cybersecurity solutions. Traditional intrusion detection methods that mainly depend on predefined rules and known attack signatures may have difficulty identifying changing or previously unseen network threats. This paper presents an AI-Based Network Intrusion Detection System that applies machine learning to identify malicious network activities from structured traffic data. The proposed system uses the NSL-KDD dataset and a Random Forest classifier to distinguish normal network connections from attack traffic. Data preprocessing operations, including feature transformation, categorical encoding, normalization, and label conversion, are performed before classification. The trained model is integrated with a Flaskbased web application that provides secure authentication, dataset uploading, intrusion prediction, visual analytics, prediction logs, and alert generation. An SQLite database is used to maintain user and prediction-related information. The implemented system was tested across authentication, dataset processing, prediction, result visualization, logging, alert generation, and logout functions, with the documented test cases successfully completed. The reported model evaluation includes accuracy, precision, recall, and F1-score, demonstrating the practical application of the proposed approach for automated network traffic analysis and intrusion detection.