Intrusion Detection System Using Machine Learning and Flask Framework

Description

This project presents the development of an Intrusion Detection System (IDS) that utilizes Random Forest and Linear Regression algorithms to detect and classify various network attacks, including DOS, probe, remote to local, and user to local. The dataset is preprocessed and balanced to handle the imbalanced nature of attack data. The IDS performance is evaluated using precision, recall, and F1-score metrics. A Flask web application is deployed to visualize the results, providing real-time monitoring and interactive insights into the network security status.