Intelligent Detection of DDoS Attacks Using Ensemble Machine Learning Techniques

The Internet has changed the world in the last few decades, but its growth has also brought about many online dangers, most notably Distributed Denial of Service (DDoS) attacks. These attacks stop servers from working by flooding them with strange traffic, which can slow them down or even cause the system to crash. In today's digitally dependent world, fighting this threat is very important. This project uses advanced machine learning (ML) methods to find DDoS attacks quickly, which are becoming a bigger problem. Because DDoS attacks change all the time, there isn't just one answer that works. Classification methods like Decision Trees, Random Forest, and KNN are used to tell the difference between regular traffic and harmful behavior after a lot of study. The main goal of the study is to create a strong DDoS monitoring system that uses machine learning techniques to look at trends in network data. The system tries to quickly spot possible DDoS attacks by learning from past data and tracking in real time. To find DDoS attack trends in network data, ensemble learning, especially the Random Forest method, is used. By using various decision trees, this method provides a strong defense against DDoS threats.