The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
A secure lightweight authentication model with interference aware routing and attack detection approach in VANET
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Comprehensive Multi-Layered Security Coverage: Unlike prior works that focus on isolated issues like routing or authentication [Kaur et al.57; Khanna et al.51], VANET-DDoSNet++ spans preprocessing, feature extraction, deep learning detection, reinforcement learning mitigation, and blockchain reporting.”
From A Hybrid Deep Learning Model for Detection and Mitigation of DDoS Attacks in VANETs (2025) · cites this paper as Kaur et al.57
Citations
Cites
Aquila Optimizer: A novel meta-heuristic optimization algorithm
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The security of vehicular ad hoc networks
Raya, M., & Hubaux, J. (2005) · Security of ad hoc and Sensor Networks · doi:10.1145/1102219.1102223 · Cited by 987
Spider Monkey Optimization algorithm for numerical optimization
Bansal, J. C., Sharma, H., Jadon, S. S., & Clerc, M. (2013) · Memetic Computing · doi:10.1007/s12293-013-0128-0 · Cited by 605
Jia, H., Peng, X., & Lang, C. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115665 · Cited by 426
Bhaladhare, P., & Jinwala, D. C. (2014) · Advances in Computer Engineering · doi:10.1155/2014/396529 · Cited by 296
Trust in VANET: A Survey of Current Solutions and Future Research Opportunities
Hussain, R., Lee, J., & Zeadally, S. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2973715 · Cited by 225
Security and Privacy Challenges in 5G-Enabled Vehicular Networks
Lai, C., Lu, R., Zheng, D., & Shen, X. (2020) · IEEE Network · doi:10.1109/mnet.001.1900220 · Cited by 186
Blockchain-based batch authentication protocol for Internet of Vehicles
Bagga, P., Sutrala, A. K., Das, A. K., & Vijayakumar, P. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101877 · Cited by 165
Improving deep neural networks with multi-layer maxout networks and a novel initialization method
Sun, W., Su, F., & Wang, L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.103 · Cited by 163
Black hole attack detection in vehicular ad-hoc network using secure AODV routing algorithm
Kumar, A., Varadarajan, V., Kumar, A., Dadheech, P., Choudhary, S. S., Kumar, V. D. A., Panigrahi, B. K., & Veluvolu, K. C. (2021) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103352 · Cited by 138
Research works printed but not linked: 2
- Abdi, H.: Congruence: congruence coefficient, RV coefficient, and mantel coefficient. Encycl. Res. Des. 3, 222–229 (2010)
- NSL-KDD dataset is acquired from, https://www.unb.ca/cic/datasets/nsl.html, Accessed Feb 2022
Cited by
El‐Shafai, W., Azar, A. T., & Ahmed, S. (2025) · IEEE Access · doi:10.1109/access.2025.3552544 · Cited by 36
A Hybrid Deep Learning Model for Detection and Mitigation of DDoS Attacks in VANETs
Jayakrishna, N., & Prasanth, N. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-15215-1 · Cited by 20
A Comprehensive Survey of Deep Learning for Authentication in Vehicular Communication
Nandy, T., & Bhattacharyya, S. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.066306 · Cited by 3
An Enhanced and Lightweight Anonymous Authentication Protocol Based on PUF for VANETs
Liu, Y., Hu, X., Gao, B., Zhu, Y., Yu, G., Yuan, Q., & Wang, Y. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3660278 · Cited by 3
Almagrabi, H., Alkahtani, H. K., Alwakeel, L., Maray, M., Almubarak, H. A., Almutairi, N., Assiri, H., & Ebad, S. A. (2026) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70397 · Cited by 1
Metadata
- Authors
- Kaur, G., & Kakkar, D.
- Affiliations
- Dr. B. R. Ambedkar National Institute of Technology Jalandhar (IN)
- Corresponding author
- Kaur, Gurjot
- ORCID
- Kakkar, Deepti
- Venue
- Cluster Computing
- Published
- 2024-11-26
- Volume
- 28
- Issue
- 2
- ISSN
- 1386-7857 (ISSN-L)
- 1573-7543
- OpenAlex
- W4404738135
- Cited by
- 14
- References
- 27
- Citations by year
- 2026: 8 · 2025: 6
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Cluster Computing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Computer Networks and Communications › Security in Wireless Sensor Networks
- Physical Sciences › Engineering › Automotive Engineering › IoT and GPS-based Vehicle Safety Systems
- Keywords
- vehicular ad hoc networks, lightweight authentication, interference-aware routing, attack detection, cluster head selection, fuzzy logic, deep maxout network, energy efficiency, trust evaluation
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: A secure lightweight authentication model with interference aware routing and attack detection approach in VANET. https://app.cochat.ai/papers/10.1007/s10586-024-04772-1
- The paper
- Kaur, G., & Kakkar, D. (2025). A secure lightweight authentication model with interference aware routing and attack detection approach in VANET. Cluster Computing. https://doi.org/10.1007/s10586-024-04772-1
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.
