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

Title
A novel intrusion detection method using deep neural network for in-vehicle network security
Authors
Kang M.-J.Kang J.-W.
Ewha Authors
강제원
SCOPUS Author ID
강제원scopus
Issue Date
2016
Journal Title
IEEE Vehicular Technology Conference
ISSN
1550-2252JCR Link
Citation
vol. 2016-July
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCOPUS scopus
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. © 2016 IEEE.
DOI
10.1109/VTCSpring.2016.7504089
ISBN
9781509016983
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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