An empirical study of intelligent approaches to DDoS detection in large scale networks

Xiaoyu Liang, Taieb Znati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Distributed Denial of Services (DDoS) attacks continue to be one of the most challenging threats to the Internet. The intensity and frequency of these attacks are increasing at an alarming rate. Numerous schemes have been proposed to mitigate the impact of DDoS attacks. This paper presents a comprehensive empirical evaluation of Machine Learning (ML)based DDoS detection techniques, to gain better understanding of their performance in different types of environments. To this end, a framework is developed, focusing on different attack scenarios, to investigate the performance of a class of ML-based techniques. The evaluation uses different performance metrics, including the impact of the 'Class Imbalance Problem' on ML-based DDoS detection. The results of the comparative analysis show that no one technique outperforms all others in all test cases. Furthermore, the results underscore the need for a method oriented feature selection model to enhance the capabilities of ML-based detection techniques. Finally, the results show that the class imbalance problem significantly impacts performance, underscoring the need to address this problem in order to enhance ML-based DDoS detection capabilities.

Original languageEnglish
Title of host publication2019 International Conference on Computing, Networking and Communications, ICNC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages821-827
Number of pages7
ISBN (Electronic)9781538692233
DOIs
Publication statusPublished - Apr 8 2019
Externally publishedYes
Event2019 International Conference on Computing, Networking and Communications, ICNC 2019 - Honolulu, United States
Duration: Feb 18 2019Feb 21 2019

Publication series

Name2019 International Conference on Computing, Networking and Communications, ICNC 2019

Conference

Conference2019 International Conference on Computing, Networking and Communications, ICNC 2019
Country/TerritoryUnited States
CityHonolulu
Period2/18/192/21/19

Keywords

  • Class Imbalance Problem
  • DDoS Detection
  • Empirical Evaluation
  • Machine Learning Techniques

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Hardware and Architecture

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