TY - GEN
T1 - Systematic intrusion detection technique for an in-vehicle network based on time-series feature extraction
AU - Suda, Hiroki
AU - Natsui, Masanori
AU - Hanyu, Takahiro
N1 - Funding Information:
The authors thank Prof. S. Nagayama and Associate Prof. H. Inoue of Hiroshima City University for excellent technical assistance. Part of this work was carried out under the Cooperative Research Project Program of the RIEC, Tohoku University, Brainware LSI Project by MEXT, JST OPERA, and JSPS KAKENHI Grant Number 16KT0187.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/19
Y1 - 2018/7/19
N2 - 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.
AB - 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.
KW - Car security
KW - Controller area network
KW - Deep learning
KW - Intrusion detection system
KW - Recurrent neural network
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U2 - 10.1109/ISMVL.2018.00018
DO - 10.1109/ISMVL.2018.00018
M3 - Conference contribution
AN - SCOPUS:85050974455
T3 - Proceedings of The International Symposium on Multiple-Valued Logic
SP - 56
EP - 61
BT - Proceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018
PB - IEEE Computer Society
T2 - 48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018
Y2 - 16 May 2018 through 18 May 2018
ER -