DDOS Attack Detection and Mitigation

Abstract

Distributed Denial of Service (DDoS) attacks have become a significant threat to the stability and availability of online services. These attacks aim to overwhelm a target server, network, or application with a flood of traffic, leading to service disruption, data breaches, and financial losses. This project focuses on developing a robust system for the detection and mitigation of DDoS attacks using advanced machine learning techniques and network analysis. The proposed solution integrates real-time traffic monitoring, anomaly detection algorithms, and adaptive response mechanisms to identify malicious traffic patterns early and respond effectively to minimize service downtime. By leveraging data from multiple layers of the network, the system enhances detection accuracy while reducing false positives. The ultimate goal of this project is to create a scalable and efficient DDoS protection framework that can proactively defend against evolving attack strategies, ensuring consistent and secure access to online resources.