Hurst parameter based anomaly detection for intrusion detection system

Song Jin Yu, Pauline Koh, Hyukmin Kwon, Dong Seong Kim, Huy Kang Kim

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

6 Citations (Scopus)

Abstract

Cyber-attack technologies have been evolved continuously. As a result, new attacks and their variants appearevery day. Also, intelligent and malicious attackers use varioustechniques to bypass the current signature and anomalydetection based intrusion detection systems. To detect thenew attacks more effectively, new anomaly detection modelis needed. In this paper, we propose a novel anomaly detectionmethod based on the self-similarity estimation of systems andnetworks. We primarily use the self-similarity property whichis characterized by the Hurst parameter. With the proposedmethod, we can detect network and system's anomaly statusby computing the change of self-similarity value. We evaluatedthe effectiveness and efficiency of our approach using the'1999 DARPA Intrusion Detection Evaluation dataset'. Also, we deployed the self-similarity based IDS in the real watergrid system.

Original languageEnglish
Title of host publicationProceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages234-240
Number of pages7
ISBN (Electronic)9781509043149
DOIs
Publication statusPublished - 2017 Mar 10
Event16th IEEE International Conference on Computer and Information Technology, CIT 2016 - Nadi, Fiji
Duration: 2016 Dec 72016 Dec 10

Other

Other16th IEEE International Conference on Computer and Information Technology, CIT 2016
CountryFiji
CityNadi
Period16/12/716/12/10

Keywords

  • Anomaly detection
  • Hurst parameter
  • Intrusion detection system

ASJC Scopus subject areas

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

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  • Cite this

    Yu, S. J., Koh, P., Kwon, H., Kim, D. S., & Kim, H. K. (2017). Hurst parameter based anomaly detection for intrusion detection system. In Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016 (pp. 234-240). [7876343] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIT.2016.98