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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“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
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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
LEPA: Low-Overhead and Efficient Privacy-Preserving Authentication Scheme in VANETs
Moni, S. S., & Manivannan, D. (2026) · Network · doi:10.3390/network6020029
Shankar, J., & Nagaraja, S. R. (2025) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis65355.2025.11070946
Deep Learning-Based Intrusion Detection Systems in VANETs: A systematic Literature Review
Bekele, H. (2025) · UTUPub (University of Turku)
A Lightweight ML Approach for VANET Attack Detection in Connected Vehicle Networks
Mittal, M., Bahuguna, R., Gupta, P., Maurya, G., Singh, A., & Shakir, A. K. (2025) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect68904.2025.11318364
Li, Z., Yang, Y., Liu, X., & Shi, B. (2026) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70373
Secure Multi-Platoon Control of Automated Vehicles Under Hidden Denial-Of-Service Attacks
Qian, Z., & Zhang, Y. (2025) · International Conference Robotics Systems and Automation Engineering · doi:10.1109/rsae65932.2025.11488096
FOC: Performance-Aware Intelligent Fuzzy-Based Optimization Clustering Framework for VANETs
Singh, D., Jain, S., Kakkar, D., & Raj, A. (2026) · International Conference on Intelligent Processing, Hardware, Electronics, and Radio Systems (CIPHER) · doi:10.1109/cipher70417.2026.11523921
Self-citationPredictive-Q Learning based Interference-and-Mobility Aware Routing for UAV Swarm Networks
Dong, Z., Zhai, D., Tao, Y., Feng, Z., & Sun, H. (2026) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc69287.2026.11579978
El‐Dalahmeh, M., & Albdour, L. (2026) · IET Communications · doi:10.1049/cmu2.70189
Satheshkumar, K., Ramalingam, S. P., Babu, A. S., & Sarojini, R. (2026) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-026-01150-y
Fuzzy Logic-Driven Intrusion Detection Framework for Secure Satellite Communication Networks
Khare, S., & Walia, N. K. (2026) · International Conference on Smart Communication and Sustainable Technologies (ICSCST) · doi:10.1109/icscst70031.2026.11709733
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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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.
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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.
