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

Min Ju Kang, Je Won Kang

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

114 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 languageEnglish
Title of host publication2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509016983
DOIs
StatePublished - 5 Jul 2016
Event83rd IEEE Vehicular Technology Conference, VTC Spring 2016 - Nanjing, China
Duration: 15 May 201618 May 2016

Publication series

NameIEEE Vehicular Technology Conference
Volume2016-July
ISSN (Print)1550-2252

Conference

Conference83rd IEEE Vehicular Technology Conference, VTC Spring 2016
Country/TerritoryChina
CityNanjing
Period15/05/1618/05/16

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