Advance Network Intrusion Detection System Using Deep Learning Techniques

With the rapid increase in cyber threats, traditional intrusion detection systems (IDS) struggle to keep up with sophisticated attacks. This project aims to develop an Advanced Network Intrusion Detection System (NIDS) using Deep Learning techniques to detect and classify network intrusions effectively. The system processes real-time network traffic and classifies it as normal or malicious using deep learning models such as ML models. The dataset is preprocessed using feature engineering techniques like One-Hot Encoding and Min-Max Scaling to improve accuracy. The trained model is deployed in a Flask-based web application that continuously monitors network activity and alerts administrators about potential threats. Unlike traditional signature-based IDS, this system can detect zero-day attacks by learning patterns from previous intrusions. By comparing multiple deep learning architectures, we aim to achieve high accuracy, precision, and recall in intrusion detection. The proposed system enhances network security and helps organizations prevent unauthorized access and data breaches effectively.