An Early Detection Mechanism for Distributed Denial of Service (DDoS) Attack Using Machine Learning Techniques

In recent years, the digitally interconnected landscape of users has witnessed varieties of cyberattacks. One such attack is the Distributed Denial of Service attack (DDoS). Organizations increasingly rely on digital platforms to conduct business and interact with their customers which has expanded the attack surface for attacks like DDoS. In this research paper we present a lightweight and robust machine learning based hybrid technique for DDoS attack detection. We use machine learning classifiers to analyze network traffic patterns to distinguish between legitimate and malicious traffic that our system receives. The proposed system achieves an impressive accuracy of 98.55% using XGBoost Algorithm. The proposed model outperforms other existing models based on the results that we provide.