Intrusion detection system using machine learning

Intrusion Detection Systems (IDS) play a vital role in identifying and preventing such malicious activities. This project presents a machine learning-based intrusion detection system designed to enhance network security by accurately detecting abnormal behavior. The proposed system uses the Random Forest algorithm to classify network traffic as normal or malicious. The dataset is preprocessed and relevant features are selected to improve the performance of the model. The system is designed with a user-friendly interface that allows users to input network parameters and obtain prediction results.