Detection of Network Intrusions using Machine Learning

About the Project The project focuses on developing a web-based Intrusion Detection System (IDS) SOC Dashboard using Python, Flask, and Machine Learning techniques. It is designed to monitor network activity, detect suspicious patterns, and present security insights through an interactive dashboard. The system integrates backend development with data analysis to provide real-time visualization of alerts and threat classifications, demonstrating practical skills in full-stack development and cybersecurity analytics. About the Research Paper The research paper presents the methodology, implementation, and evaluation of the IDS SOC Dashboard. It explains the problem statement, related work, system architecture, machine learning approach, and experimental results. The objective of the paper is to document how intelligent data-driven techniques can enhance cybersecurity monitoring while showcasing the scalability and effectiveness of combining Flask-based web systems with machine learning models.