A Machine Learning Framework for Identifying and Preventing DDoS Attacks in Real-Time

Abstract

Distributed Denial of Service (DDoS) attacks are a significant threat to network security, disrupting services by overwhelming systems with malicious traffic. Traditional methods of mitigating these attacks are often ineffective due to their reliance on static rules or manual intervention, which can be slow and limited in adaptability. To address this, we developed an automated, real-time DDoS detection system using machine learning to enhance efficiency and reliability.

Our system utilizes a combination of Random Forest, Neural Networks, and Logistic Regression models to analyze network traffic and detect DDoS attacks with high accuracy. Implemented with Python in a Flask-based application, this solution leverages machine learning algorithms to identify complex patterns associated with malicious activity. The result is a robust, scalable system capable of rapidly distinguishing between normal and attack traffic, helping to secure networks against evolving threats.