The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
In-vehicle network intrusion detection using deep convolutional neural network
Abstract
Abstract The implementation of electronics in modern vehicles has resulted in an increase in attacks targeting in-vehicle networks; thus, attack detection models have caught the attention of the automotive industry and its researchers. Vehicle network security is an urgent and significant problem because the malfunctioning of vehicles can directly affect human and road safety. The controller area network (CAN), which is used as a de facto standard for in-vehicle networks, does not have sufficient security features, such as message encryption and sender authentication, to protect the network from cyber-attacks. In this paper, we propose an intrusion detection system (IDS) based on a deep convolutional neural network (DCNN) to protect the CAN bus of the vehicle. The DCNN learns the network traffic patterns and detects malicious traffic without hand-designed features. We designed the DCNN model, which was optimized for the data traffic of the CAN bus, to achieve high detection performance while reducing the unnecessary complexity in the architecture of the Inception-ResNet model. We performed an experimental study using the datasets we built with a real vehicle to evaluate our detection system. The experimental results demonstrate that the proposed IDS has significantly low false negative rates and error rates when compared to the conventional machine-learning algorithms.
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Cites
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Malware traffic classification using convolutional neural network for representation learning
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Cited by
Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey
Zhang, J., Pan, L., Han, Q.-L., Chen, C., Wen, S., & Xiang, Y. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1004261 · Cited by 432
Javed, A. R., Rehman, S. U., Khan, M. U., Alazab, M., & G, T. R. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3059881 · Cited by 331
Ashraf, J., Bakhshi, A. D., Moustafa, N., Khurshid, H., Javed, A., & Beheshti, A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3017882 · Cited by 295
Intelligent transportation system for internet of vehicles based vehicular networks for smart cities
Rani, P., & Sharma, R. (2023) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2022.108543 · Cited by 237
Federated Intrusion Detection in Blockchain-Based Smart Transportation Systems
Abdel-Basset, M., Moustafa, N., Hawash, H., Razzak, I., Sallam, K. M., & Elkomy, O. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3119968 · Cited by 161
Metadata
- Authors
- Song, H. M., Woo, J., & Kim, H. K.
- Venue
- Vehicular Communications
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 21
- Pages
- 100198
- ISSN
- 2214-2096 (ISSN-L)
- OpenAlex
- W2979202956
- Cited by
- 586
- References
- 38
- FWCI
- 24.37
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
