Detection of Distributed Denial of Service Attacks based on Machine Learning Algorithms

TL;DR

This paper explores the use of various machine learning algorithms, especially SVM, to detect DDoS attacks effectively, achieving high detection accuracy and improving upon existing methods.

Contribution

It applies multiple ML techniques to DDoS detection and demonstrates that SVM achieves 97.1% accuracy, outperforming previous approaches.

Findings

SVM detects 97.1% of DDoS attacks.

Data bytes are more similar in DDoS attacks than benign attempts.

Proposed approach improves detection accuracy over existing methods.

Abstract

Distributed Denial of Service (DDoS) attacks make the challenges to provide the services of the data resources to the web clients. In this paper, we concern to study and apply different Machine Learning (ML) techniques to separate the DDoS attack instances from benign instances. Our experimental results show that forward and backward data bytes of our dataset are observed more similar for DDoS attacks compared to the data bytes for benign attempts. This paper uses different machine learning techniques for the detection of the attacks efficiently in order to make sure the offered services from web servers available. This results from the proposed approach suggest that 97.1% of DDoS attacks are successfully detected by the Support Vector Machine (SVM). These accuracies are better while comparing to the several existing machine learning approaches.