The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Entropy-based anomaly detection for in-vehicle networks
Abstract
Due to an increased connectivity and seamless integration of information technology into modern vehicles, a trend of research in the automotive domain is the development of holistic IT security concepts. Within the scope of this development, vehicular attack detection is one concept which gains an increased attention, because of its reactive nature that allows to respond to threats during runtime. In this paper we explore the applicability of entropy-based attack detection for in-vehicle networks. We illustrate the crucial aspects for an adaptation of such an approach to the automotive domain. Moreover, we show first exemplary results by applying the approach to measurements derived from a standard vehicle's CAN-Body network.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Cited by
In-vehicle network intrusion detection using deep convolutional neural network
Song, H. M., Woo, J., & Kim, H. K. (2020) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100198 · Cited by 586
GIDS: GAN based Intrusion Detection System for In-Vehicle Network
Seo, E., Song, H. M., & Kim, H. K. (2018) · 2018 16th Annual Conference on Privacy, Security and Trust (PST) · doi:10.1109/pst.2018.8514157 · Cited by 533
A Survey of Intrusion Detection for In-Vehicle Networks
Wu, W., Li, R., Xie, G., An, J., Bai, Y., Zhou, J., & Li, K. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2908074 · Cited by 394
VoltageIDS: Low-Level Communication Characteristics for Automotive Intrusion Detection System
Choi, W., Joo, K., Jo, H. J., Park, M. C., & Lee, D. H. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2812149 · Cited by 312
A review on safety failures, security attacks, and available countermeasures for autonomous vehicles
Cui, J., Liew, L. S., Sabaliauskaite, G., & Zhou, F. (2019) · Ad Hoc Networks · doi:10.1016/j.adhoc.2018.12.006 · Cited by 298
Metadata
- Authors
- Muter, M., & Asaj, N.
- Venue
- 2011 IEEE Intelligent Vehicles Symposium (IV)
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 1110-1115
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2133854595
- Cited by
- 378
- References
- 1
- FWCI
- 1.43
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Muter, M., & Asaj, N. (2011). Entropy-based anomaly detection for in-vehicle networks. 2011 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2011.5940552
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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.
