Systematic intrusion detection technique for an in-vehicle network based on time-series feature extraction

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

4 Citations (Scopus)

Abstract

In this paper, we propose a systematic intrusion detection algorithm based on time-series feature extraction for an in-vehicle network. Since packet-Type valid data are transmitted inside an in-vehicle network periodically, illegal data due to unauthorized intrusion attack can be easily and uniformly detected by using periodical time-series feature of valid data, where recurrent neural network is a key tool to efficiently extract their time-series feature. In fact, through an evaluation using data acquired from actual vehicles, we show that the proposed method can detect typical intrusion attack patterns such as data modification attack and injection attack.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018
PublisherIEEE Computer Society
Pages56-61
Number of pages6
ISBN (Electronic)9781538644638
DOIs
Publication statusPublished - 2018 Jul 19
Event48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018 - Linz, Austria
Duration: 2018 May 162018 May 18

Publication series

NameProceedings of The International Symposium on Multiple-Valued Logic
Volume2018-May
ISSN (Print)0195-623X

Other

Other48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018
CountryAustria
CityLinz
Period18/5/1618/5/18

Keywords

  • Car security
  • Controller area network
  • Deep learning
  • Intrusion detection system
  • Recurrent neural network

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    Suda, H., Natsui, M., & Hanyu, T. (2018). Systematic intrusion detection technique for an in-vehicle network based on time-series feature extraction. In Proceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018 (pp. 56-61). (Proceedings of The International Symposium on Multiple-Valued Logic; Vol. 2018-May). IEEE Computer Society. https://doi.org/10.1109/ISMVL.2018.00018