Published Paper Details:

This project aims to develop a web application using Flask, which integrates machine learning functionalities for network intrusion detection. The application will provide a user interface enabling users to load data, preprocess it, select models, and predict intrusions. Leveraging libraries such as Pandas, NumPy, and Scikit-learn, the application facilitates tasks such as data handling, encoding categorical features, splitting datasets for training and testing, and evaluating models. The machine learning algorithms available include Decision Trees, Random Forests, Support Vector Machines, and a pre-trained Neural Network. Users will be able to input network traffic features to predict intrusions at a certain time, with the system classifying potential intrusions into distinct attack categories. The ultimate goal of this project is to offer a user-friendly tool for network security analysts to identify and classify potential network intrusions.