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

研究成果: Conference contribution

4 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトルProceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018
出版者IEEE Computer Society
ページ56-61
ページ数6
ISBN(電子版)9781538644638
DOI
出版物ステータスPublished - 2018 7 19
イベント48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018 - Linz, Austria
継続期間: 2018 5 162018 5 18

出版物シリーズ

名前Proceedings of The International Symposium on Multiple-Valued Logic
2018-May
ISSN(印刷物)0195-623X

Other

Other48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018
Austria
Linz
期間18/5/1618/5/18

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • これを引用

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