A Novel Intrusion Detection Method Using Deep Neural Network for In-Vehicle Network Security

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 pa...

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Bibliographic Details
Published in2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) pp. 1 - 5
Main Authors Min-Ju Kang, Je-Won Kang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2016
Subjects
Feature extraction
Intrusion detection
Monitoring
Neural networks
Syntactics
Testing
Training
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Summary: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.
DOI:10.1109/VTCSpring.2016.7504089