A novel intrusion detection method using deep neural network for in-vehicle network security

Min Ju Kang; Je Won Kang

doi:10.1109/VTCSpring.2016.7504089

A novel intrusion detection method using deep neural network for in-vehicle network security

Min Ju Kang, Je Won Kang

Electronic and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

151 Scopus citations

Abstract

In this paper, we propose a novel intrusion detection technique using a deep neural network (DNN). In the proposed technique, in-vehicle network packets exchanged between electronic control units (ECU) are trained to extract low- dimensional features and used for discriminating normal and hacking packets. The features perform in high efficient and low complexity because they are generated directly from a bitstream over the network. The proposed technique monitors an exchanging packet in the vehicular network while the feature are trained off-line, and provides a real-time response to the attack with a significantly high detection ratio in our experiments.

Original language	English
Title of host publication	2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509016983
DOIs	https://doi.org/10.1109/VTCSpring.2016.7504089
State	Published - 5 Jul 2016
Event	83rd IEEE Vehicular Technology Conference, VTC Spring 2016 - Nanjing, China Duration: 15 May 2016 → 18 May 2016

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2016-July
ISSN (Print)	1550-2252

Conference

Conference	83rd IEEE Vehicular Technology Conference, VTC Spring 2016
Country/Territory	China
City	Nanjing
Period	15/05/16 → 18/05/16

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF- 2014R1A1A2056587).

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/VTCSpring.2016.7504089

Cite this

Kang, M. J., & Kang, J. W. (2016). A novel intrusion detection method using deep neural network for in-vehicle network security. In 2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings Article 7504089 (IEEE Vehicular Technology Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCSpring.2016.7504089

@inproceedings{204c55bec9314c1d9e17abae8de78aba,

title = "A novel intrusion detection method using deep neural network for in-vehicle network security",

abstract = "In this paper, we propose a novel intrusion detection technique using a deep neural network (DNN). In the proposed technique, in-vehicle network packets exchanged between electronic control units (ECU) are trained to extract low- dimensional features and used for discriminating normal and hacking packets. The features perform in high efficient and low complexity because they are generated directly from a bitstream over the network. The proposed technique monitors an exchanging packet in the vehicular network while the feature are trained off-line, and provides a real-time response to the attack with a significantly high detection ratio in our experiments.",

author = "Kang, {Min Ju} and Kang, {Je Won}",

note = "Funding Information: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF- 2014R1A1A2056587). Publisher Copyright: {\textcopyright} 2016 IEEE.; 83rd IEEE Vehicular Technology Conference, VTC Spring 2016 ; Conference date: 15-05-2016 Through 18-05-2016",

year = "2016",

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day = "5",

doi = "10.1109/VTCSpring.2016.7504089",

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Kang, MJ & Kang, JW 2016, A novel intrusion detection method using deep neural network for in-vehicle network security. in 2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings., 7504089, IEEE Vehicular Technology Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., 83rd IEEE Vehicular Technology Conference, VTC Spring 2016, Nanjing, China, 15/05/16. https://doi.org/10.1109/VTCSpring.2016.7504089

A novel intrusion detection method using deep neural network for in-vehicle network security. / Kang, Min Ju; Kang, Je Won.
2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7504089 (IEEE Vehicular Technology Conference; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In this paper, we propose a novel intrusion detection technique using a deep neural network (DNN). In the proposed technique, in-vehicle network packets exchanged between electronic control units (ECU) are trained to extract low- dimensional features and used for discriminating normal and hacking packets. The features perform in high efficient and low complexity because they are generated directly from a bitstream over the network. The proposed technique monitors an exchanging packet in the vehicular network while the feature are trained off-line, and provides a real-time response to the attack with a significantly high detection ratio in our experiments.

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A novel intrusion detection method using deep neural network for in-vehicle network security

Abstract

Publication series

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Bibliographical note

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Cite this