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 investigatesfortifying defenses against such malicious invasions. The project incorporates a user-friendly UI featuringtwobuttons: one for uploading captured traf ic files and another for analysis to classify whether it's a DDoS attack. Thebackground of the problem aspires to a robust and adaptive DDoS detection system to ensure the continuityof online services [14]. To resolve this, the project proposes an automated DDoS attack detection mechanismpoweredby Machine Learning and Artificial Intelligence. The application involves two pivotal experiments: the first assessesmodel accuracy, highlighting the Decision Tree as the most promising, while the second focuses on preventingoverfitting during training, and the Random Forest Classifier stands out to this one [15]. The challengesencountered were mitigated through techniques like early stopping and regularization. The model's applicationacross various scenarios showcased its potential for ef ective real-time DDoS detection.

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