TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification

Abstract

Distributed Denial of Service (DDoS) attack is one of the most dangerous attacks that result in bringing down the server(s) and it is essential to classify the exact attack to implement robust security measures. In this work, we present an approach for detecting the prominent DDoS attacks that can be carried over Transport Layer protocols. Four different levels are taken into consideration which helps to classify one of the 11 different attacks. A bigger problem is divided into smaller ones and then conquered. This approach, called TaxoDaCML - Taxonomy-based Divide and Conquer approach using ML minimizes computational cost and at the same time maintains the required accuracy. Results prove that our approach achieves 99.9% accuracy for DDoS attack detection and more than 85% for DDoS attack classification. Comparison of TaxoDaCML is done with the previous works and is found to perform better for DDoS attacks classification.

Keywords

• Information management security
• Machine learning
• Distributed denial of service
• Reflection attack
• Exploitation attack