An intrusion detection system based on polynomial feature correlation analysis

Qingru Li, Zhiyuan Tan, Aruna Jamdagni, Priyadarsi Nanda, Xiangjian He, Wei Han

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation results show that the proposed system achieved better detection rates on KDD Cup 99 data set in comparison with another two state-of-the-art detection schemes. Moreover, the computational complexity of the system has been analysed in this paper and shows similar to the two state-of-the-art schemes.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages978-983
Number of pages6
ISBN (Electronic)9781509049059
DOIs
Publication statusPublished - 7 Sep 2017
Externally publishedYes
Event16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017 - Sydney, Australia
Duration: 1 Aug 20174 Aug 2017

Publication series

NameProceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017

Conference

Conference16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
Country/TerritoryAustralia
CitySydney
Period1/08/174/08/17

Keywords

  • Computational complexity
  • Feature correlation analysis
  • Intrusion Detection System (IDS)
  • Mahalanobis distance
  • Polynomial

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Software
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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