A novel intrusion detection method using deep neural network for in-vehicle network security
- 등재 SCOPUS
- 발행기관 Institute of Electrical and Electronics Engineers Inc.
- 발행년도 2016
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000139618
- ISBN 9781509016983
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/VTCSpring.2016.7504089
초록/요약
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.
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