Network Based Intrusion Detection System Using Machine Learning

As cyber threats continue to evolve in complexity and frequency, conventional intrusion detection systems (IDS) often fail to detect advanced and novel attacks. This project introduces an intelligent Network Intrusion Detection System (NIDS) leveraging Deep Learning to efficiently identify and categorize network intrusions. It analyzes live network traffic and determines whether it is legitimate or malicious using advanced machine learning models. Prior to training, the dataset undergoes preprocessing with techniques like One-Hot Encoding and Min-Max Scaling to enhance model performance. The final model is integrated into a Flask-based web application that provides real-time monitoring and alerts for suspicious activity. Unlike traditional IDS that rely on predefined signatures, this solution is capable of identifying zero-day threats by recognizing behavioral patterns in historical data. By evaluating and comparing different deep learning architectures, the system strives for superior detection metrics such as accuracy, precision, and recall. Ultimately, this approach aims to strengthen organizational cybersecurity and minimize the risk of data breaches and unauthorized access.