A Result Paper on “SVM-RF Sentinel: Adaptive DDoS Detection”

Abstract

DDoS (Distributed Denial of Service) attacks are one of the biggest threats to online services, such as websites, servers, and applications. These attacks flood systems with fake traffic, causing slowdowns, crashes, and major disruptions. This can lead to significant financial losses, damage to a company’s reputation, and a poor user experience. Traditional methods to detect these attacks often struggle with the size, speed, and complexity of modern DDoS attacks, making it hard to protect systems effectively. This project develops a new DDoS detection system that uses advanced machine learning to overcome these limitations. The system employs two powerful algorithms: Support Vector Machine (SVM) and Random Forest. SVM is used for its strong ability to classify and identify patterns of malicious traffic, while Random Forest helps manage and analyze large datasets more effectively. By using these algorithms, the system enhances detection accuracy A key feature is its easy-to-use interface, which allows both technical and non-technical users to set up, monitor, and respond to security alerts without needing extensive training. This project offers a more accurate, faster, and secure method for detecting and managing DDoS attacks. By using advanced machine learning with enhanced security features, it provides a robust solution to one of the most challenging problems in network security today.