ARCHIVES

Distributed Denial of Service (DDoS) attacks pose a significant threat to network security by overwhelming a target system with a massive volume of traffic, disrupting legitimate user access. This project presents a DDoS Attack Detection System that utilizes machine learning-based traffic analysis to identify potential attack patterns. The system generates synthetic network traffic data, allowing for model training and evaluation. The web-based interface, built using Flask and JavaScript, enables users to upload traffic datasets, visualize network activity through real-time charts, and receive attack alerts. The detection mechanism classifies network traffic as normal or malicious based on key parameters like packet count and unique IP addresses. Additionally, a mitigation feature allows users to block detected malicious IPs, preventing further attacks. This system provides a user-friendly dashboard with dark-themed aesthetics and interactive elements for an intuitive experience. The integration of data visualization, real-time monitoring, and mitigation makes this project a robust solution for enhancing network security against DDoS attacks.