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Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

Title
Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security
Authors
Kang, Min-JooKang, Je-Won
Ewha Authors
강제원
SCOPUS Author ID
강제원scopus
Issue Date
2016
Journal Title
PLOS ONE
ISSN
1932-6203JCR Link
Citation
vol. 11, no. 6
Publisher
PUBLIC LIBRARY SCIENCE
Indexed
SCIE; SCOPUS WOS scopus
Abstract
A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.
DOI
10.1371/journal.pone.0155781
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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