PLoS One. 2016; 11(6): e0155781.
PMCID: PMC4896428
Published online 2016 Jun 7. doi: 10.1371/journal.pone.0155781
Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security Min-Joo Kang and Je-Won Kang* Tieqiao Tang, Editor The Department of Electronics Engineering, Ewha W. University, Seoul, Republic of Korea Beihang University, CHINA Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: MJK JWK. Performed the experiments: MJK JWK. Analyzed the data: MJK JWK. Contributed reagents/materials/analysis tools: MJK JWK. Wrote the paper: MJK JWK. * E-mail:
[email protected] Received 2016 Jan 12; Accepted 2016 May 4. Copyright © 2016 Kang, Kang This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article has been cited by other articles in PMC.
Abstract
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Introduction
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Related Work
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CAN
Intrusion Detection with Machine Learning
Deep Learning for Classification
Proposed Technique
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Proposed Intrusion Detection System with Deep Neural Network Structure
CAN Packet Feature
Training the Deep Neural Network Structure
Attack Detection
Experimental Results
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Data Set
Performance Evaluation
Conclusion
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Supporting Information
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S1 File
Acknowledgments
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Funding Statement
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Data Availability
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References
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