DDOS ATTACK DETECTION AND MITIGATION

Abstract

The rapid growth of internet-based services and cloud computing has significantly increased the vulnerability of networks to Distributed Denial of Service (DDoS) attacks, which aim to overwhelm systems by flooding them with excessive traffic. These attacks can disrupt services, degrade performance, and cause substantial financial and reputational damage to organizations. Traditional security mechanisms often struggle to detect and mitigate DDoS attacks in real time due to the dynamic and distributed nature of modern networks. To address these challenges, this project proposes an intelligent system for DDoS Attack Detection and Mitigation using advanced machine learning and network monitoring techniques. The proposed system continuously monitors network traffic and extracts key features such as packet rate, source IP distribution, protocol types, and traffic patterns. Through data preprocessing and feature engineering, the system transforms raw network data into structured inputs suitable for machine learning models. Algorithms such as Random Forest, Support Vector Machines (SVM), and Deep Learning models are employed to classify traffic as normal or malicious. The system is capable of identifying abnormal spikes and traffic anomalies that indicate potential DDoS attacks. Once an attack is detected, a mitigation module is activated, which can implement strategies such as traffic filtering, rate limiting, IP blocking, and load balancing to reduce the impact of the attack. Experimental results demonstrate that the proposed system achieves high detection accuracy with low false positive rates, while maintaining real-time performance. The integration of intelligent detection with automated mitigation ensures quick response and enhanced network resilience. Overall, this project provides a scalable, efficient, and proactive solution for protecting network infrastructure against DDoS threats, making it highly suitable for modern cloud-based and enterprise environments.