A system for denial-of-service attack detection based on multivariate correlation analysis

Zhiyuan Tan, Aruna Jamdagni, Xiangjian He, Priyadarsi Nanda, Ren Ping Liu

Research output: Journal PublicationArticlepeer-review

189 Citations (Scopus)

Abstract

Interconnected systems, such as Web servers, database servers, cloud computing servers and so on, are now under threads from network attackers. As one of most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses multivariate correlation analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area-based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 data set, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy.

Original languageEnglish
Article number6519239
Pages (from-to)447-456
Number of pages10
JournalIEEE Transactions on Parallel and Distributed Systems
Volume25
Issue number2
DOIs
Publication statusPublished - Feb 2014
Externally publishedYes

Keywords

  • Denial-of-service attack
  • multivariate correlations
  • network traffic characterization
  • triangle area

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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