CLASSIFYING PREDICTING DDOS ATTACKS USING MACHINE LEARNING

Abstract

Distributed Denial of Service (DDoS) attacks pose a significant threat to network security, targeting critical infrastructure and disrupting services. Traditional methods of mitigating DDoS attacks rely on signature-based detection and traffic filtering, which may not be effective against sophisticated and evolving attack techniques. In this project, we propose a novel approach for classifying and predicting DDoS attacks using machine learning algorithms. By analyzing network traffic patterns and extracting relevant features, such as packet headers, flow characteristics, and payload content, we train machine learning models to differentiate between normal traffic and DDoS attacks. Additionally, we develop predictive models to forecast potential DDoS attacks based on historical data and real-time network monitoring. Our experimental results demonstrate the effectiveness of the proposed approach in accurately classifying and predicting DDoS attacks, thereby enhancing network security and enabling proactive defense strategies.