The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
P2DAP — Sybil Attacks Detection in Vehicular Ad Hoc Networks
Abstract
Vehicular ad hoc networks (VANETs) are being increasingly advocated for traffic control, accident avoidance, and management of parking lots and public areas. Security and privacy are two major concerns in VANETs. Unfortunately, in VANETs, most privacy-preserving schemes are vulnerable to Sybil attacks, whereby a malicious user can pretend to be multiple (other) vehicles. In this paper, we present a lightweight and scalable protocol to detect Sybil attacks. In this protocol, a malicious user pretending to be multiple (other) vehicles can be detected in a distributed manner through passive overhearing by s set of fixed nodes called road-side boxes (RSBs). The detection of Sybil attacks in this manner does not require any vehicle in the network to disclose its identity; hence privacy is preserved at all times. Simulation results are presented for a realistic test case to highlight the overhead for a centralized authority such as the DMV, the false alarm rate, and the detection latency. The results also quantify the inherent trade-off between security, i.e., the detection of Sybil attacks and detection latency, and the privacy provided to the vehicles in the network. From the results, we see our scheme being able to detect Sybil attacks at low overhead and delay, while preserving privacy of vehicles.
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
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Cited by
VANet security challenges and solutions: A survey
Hasrouny, H., Samhat, A. E., Bassil, C., & Laouiti, A. (2017) · Vehicular Communications · doi:10.1016/j.vehcom.2017.01.002 · Cited by 642
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2954595 · Cited by 620
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
A survey on internet of vehicles: Applications, security issues & solutions
Sharma, S., & Kaushik, B. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100182 · Cited by 277
Securing Vehicle-to-Everything (V2X) Communication Platforms
Hasan, M., Mohan, S., Shimizu, T., & Lu, H. (2020) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2020.2987430 · Cited by 275
Metadata
- Authors
- Zhou, T., Choudhury, R. R., Ning, P., & Chakrabarty, K.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2011-03
- Volume
- 29
- Issue
- 3
- Pages
- 582-594
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W2144749388
- Cited by
- 160
- References
- 1
- FWCI
- 7.58
- 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: P2DAP — Sybil Attacks Detection in Vehicular Ad Hoc Networks. https://app.cochat.ai/papers/10.1109/jsac.2011.110308
- The paper
- Zhou, T., Choudhury, R. R., Ning, P., & Chakrabarty, K. (2011). P2DAP — Sybil Attacks Detection in Vehicular Ad Hoc Networks. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/jsac.2011.110308
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.