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Towards resilient critical infrastructures: Application of Type-2 Fuzzy Logic in embedded network security cyber sensor

  • Ondrej Linda
  • , Milos Manic
  • , Jim Alves-Foss
  • , Todd Vollmer

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

20 Scopus citations

Abstract

Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meaning of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL provides a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber security test-bed.

Original languageEnglish
Title of host publicationProceedings - ISRCS 2011
Subtitle of host publication4th International Symposium on Resilient Control Systems
Pages26-32
Number of pages7
DOIs
StatePublished - 2011
Externally publishedYes
Event4th International Symposium on Resilient Control Systems, ISRCS 2011 - Boise, ID, United States
Duration: Aug 9 2011Aug 11 2011

Publication series

NameProceedings - ISRCS 2011: 4th International Symposium on Resilient Control Systems

Conference

Conference4th International Symposium on Resilient Control Systems, ISRCS 2011
Country/TerritoryUnited States
CityBoise, ID
Period08/9/1108/11/11

Keywords

  • Anomaly Detection
  • Cyber Sensor
  • Embedded Systems
  • Online Clustering
  • Type-2 Fuzzy Logic

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