Xiangfeiyang Li¹ and Jonathan Thamrun², ¹Fairmont Prep Academy, USA, ²California State Polytechnic University, USA

In response to the increasing threat of DDoS (Distributed Denial of Service) attacks, this project investigates fortifying defenses against such malicious invasions. The project incorporates a user-friendly UI featuring two buttons: one for uploading captured traf ic files and another for analysis to classify whether it's a DDoS attack. The background of the problem aspires to a robust and adaptive DDoS detection system to ensure the continuity of online services [14]. To resolve this, the project proposes an automated DDoS attack detection mechanism powered by Machine Learning and Artificial Intelligence. The application involves two pivotal experiments: the first assesses model accuracy, highlighting the Decision Tree as the most promising, while the second focuses on preventing over fitting during training, and the Random Forest Classifier stands out to this one [15]. The challenges encountered were mitigated through techniques like early stopping and regularization. The model's application across various scenarios showcased its potential for efective real-time DDoS detection.

DDoS Attack, Detection System, Artificial Intelligence, Recognization & Prevention.