The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Detecting and correcting malicious data in VANETs
Abstract
In order to meet performance goals, it is widely agreed that vehicular ad hoc networks (VANETs) must rely heavily on node-to-node communication, thus allowing for malicious data traffic. At the same time, the easy access to information afforded by VANETs potentially enables the difficult security goal of data validation. We propose a general approach to evaluating the validity of VANET data. In our approach a node searches for possible explanations for the data it has collected based on the fact that malicious nodes may be present. Explanations that are consistent with the node's model of the VANET are scored and the node accepts the data as dictated by the highest scoring explanations. Our techniques for generating and scoring explanations rely on two assumptions: 1) nodes can tell "at least some" other nodes apart from one another and 2) a parsimony argument accurately reflects adversarial behavior in a VANET. We justify both assumptions and demonstrate our approach on specific VANETs.
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
Douceur, J. R. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45748-8_24 · Cited by 4,419
Mitigating routing misbehavior in mobile ad hoc networks
Marti, S., Giuli, T. J., Lai, K., & Baker, M. (2000) · Proceedings of the 6th annual international conference on Mobile computing and networking · doi:10.1145/345910.345955 · Cited by 3,415
Secure routing in wireless sensor networks: attacks and countermeasures
Karlof, C., & Wagner, D. (2003) · Ad Hoc Networks · doi:10.1016/s1570-8705(03)00008-8 · Cited by 2,937
Packet leashes: a defense against wormhole attacks in wireless networks
Hu, Y.-C., Perrig, A., & Johnson, D. B. (2004) · IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428) · doi:10.1109/infcom.2003.1209219 · Cited by 1,513
The sybil attack in sensor networks: analysis & defenses
Newsome, J., Shi, E., Song, D., & Perrig, A. (2004) · Proceedings of the 3rd international symposium on Information processing in sensor networks · doi:10.1145/984622.984660 · Cited by 1,305
Secure routing in wireless sensor networks: attacks and countermeasures
Karlof, C., & Wagner, D. (2003) · Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003. · doi:10.1109/snpa.2003.1203362 · Cited by 1,001
Brands, S., & Chaum, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/3-540-48285-7_30 · Cited by 804
Talking to Strangers: Authentication in Ad-Hoc Wireless Networks.
Balfanz, D., Smetters, D. K., Stewart, P. A., & Wong, H. C. (2002) · Cited by 633
Secure verification of location claims
Sastry, N., Shankar, U., & Wagner, D. (2003) · Proceedings of the 2nd ACM workshop on Wireless security · doi:10.1145/941311.941313 · Cited by 604
Using Directional Antennas to Prevent Wormhole Attacks.
Hu, L., & Evans, D. (2004) · Cited by 532
Cited by
Karagiannis, G., Altintas, O., Ekici, E., Heijenk, G., Jarupan, B., Lin, K., & Weil, T. (2011) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2011.061411.00019 · Cited by 1,529
Survey on blockchain for Internet of Things
Wang, X., Zha, X., Ni, W., Liu, R. P., Guo, Y. J., Niu, X., & Zheng, K. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.01.006 · Cited by 567
Survey on VANET security challenges and possible cryptographic solutions
Mejri, M. N., Ben-Othman, J., & Hamdi, M. (2014) · Vehicular Communications · doi:10.1016/j.vehcom.2014.05.001 · Cited by 560
A Security and Privacy Review of VANETs
Qu, F., Wu, Z., Wang, F., & Cho, W. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2439292 · Cited by 478
Eviction of Misbehaving and Faulty Nodes in Vehicular Networks
Raya, M., Papadimitratos, P., Aad, I., Jungels, D., & Hubaux, J.-P. (2007) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2007.071006 · Cited by 406
Metadata
- Authors
- Golle, P., Greene, D., & Staddon, J.
- Venue
- Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks
- Publisher
- ACM
- Published
- 2004-10
- Pages
- 29-37
- OpenAlex
- W2147510259
- Cited by
- 527
- References
- 14
- FWCI
- 23.19
- Type
- conference paper
- 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: Detecting and correcting malicious data in VANETs. https://app.cochat.ai/papers/10.1145/1023875.1023881
- The paper
- Golle, P., Greene, D., & Staddon, J. (2004). Detecting and correcting malicious data in VANETs. Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks. https://doi.org/10.1145/1023875.1023881
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.
