Enhancing Cybersecurity: A Fusion Approach of Artificial Neural Networks and Decision Trees for Robust Imbalanced DDoS Attack Detection
Ankita Kumari, Deepali Gupta, Mudita Uppal · 2024
An important danger to internet services is DDoS attacks, which try to block access by sending too much data to specific sites. These attacks are able to go undetected because they use flaws in the way the network is set up. Protocol attacks, application-layer attacks, and massive flooding are all types of DDoS attacks that can make systems much harder to reach and less effective. Because online services are becoming more and more popular, controlling the effects of Distributed Denial of Service attacks is still a very important safety problem. Artificial Neural Networks (ANN) and Decision Tree filters are used together in the suggested model to find DDoS attacks. Using this effective strategy, combining two different strategies makes it easier to find things. When Artificial Neural Network (ANN) and Decision Tree algorithms are put together, they make a full DDoS detection model. Even though the Artificial Neural Network (ANN) can spot complicated patterns, the Decision Tree makes the decision-making process more clear. Using a thorough method improves the model's general ability to handle and understand problems, making it a powerful tool for stopping DDoS attacks in a variety of network settings.