An Intelligent DDoS Detection System to recognize and Prevent DDoS Attacks

An Intelligent DDoS Detection System to recognize and Prevent DDoS Attacks using Artificial Intelligence and Machine Learning

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

Abstract

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.

Keywords

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

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