The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
VoltageIDS: Low-Level Communication Characteristics for Automotive Intrusion Detection System
Abstract
The proliferation of computerized functions aimed at enhancing drivers' safety and convenience has increased the number of vehicular attack surfaces accordingly. The fundamental vulnerability is caused by the fact that the controller area network protocol, a de facto standard for in-vehicle networks, does not support message origin authentication. Several methods to resolve this problem have been suggested. However, most of them require modification of the CAN protocol and have their own vulnerabilities. In this paper, we focus on securing in-vehicle CAN networks, proposing a novel automotive intrusion detection system (so-called VoltageIDS). The system leverages the inimitable characteristics of an electrical CAN signal as a fingerprint of the electronic control units. The noteworthy contributions are that VoltageIDS does not require any modification of the current system and has been validated on actual vehicles while driving on the road. VoltageIDS is also the first automotive intrusion detection system capable of distinguishing between errors and the bus-off attack. Our experimental results on a CAN bus prototype and on real vehicles show that VoltageIDS detects intrusions in the in-vehicle CAN network. Moreover, we evaluate VoltageIDS while a vehicle is moving.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,544
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,715
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
An introduction to kernel-based learning algorithms
Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., & Scholkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,473
A Practical Approach to Feature Selection
Kira, K., & Rendell, L. A. (1992) · Machine Learning Proceedings 1992 · doi:10.1016/b978-1-55860-247-2.50037-1 · Cited by 2,978
Anomaly-based network intrusion detection: Techniques, systems and challenges
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009) · Computers & Security · doi:10.1016/j.cose.2008.08.003 · Cited by 2,085
Laplacian Score for Feature Selection
He, X., Cai, D., & Niyogi, P. (2005) · Cited by 1,962
Cited by
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
Automated detection-in-depth in industrial control systems
Jadidi, Z., Foo, E., Hussain, M., & Fidge, C. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08001-6 · Cited by 31
PIL-MDRS: Physical Intrusion Localization Based on Multidevice Reflection Signals in ICS
Liu, Y., Meng, L., Wang, X., Qiu, S., Lv, Z., Liu, P., & Liu, T. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3507204 · Cited by 2
Metadata
- Authors
- Choi, W., Joo, K., Jo, H. J., Park, M. C., & Lee, D. H.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2018-08
- Volume
- 13
- Issue
- 8
- Pages
- 2114-2129
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2790772609
- Cited by
- 312
- References
- 39
- FWCI
- 18.83
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: VoltageIDS: Low-Level Communication Characteristics for Automotive Intrusion Detection System. https://app.cochat.ai/papers/10.1109/tifs.2018.2812149
- The paper
- Choi, W., Joo, K., Jo, H. J., Park, M. C., & Lee, D. H. (2018). VoltageIDS: Low-Level Communication Characteristics for Automotive Intrusion Detection System. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2018.2812149
Data sources
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.
