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RETRACTED ARTICLE: Sybil Attack with RSU Detection and Location Privacy in Urban VANETs: An Efficient EPORP Technique
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Abstract
In recent years, Vehicular ad hoc networks (VANETs) could facilitate the decision-making progress of the drivers for example trip planning with the consideration of traffic. In the VANET, the Sybil attack is a very serious attack that collapses the security. In literature, some of the methods are reviewed to detect Sybil attacks in VANETs, but it fails to achieve Sybil attack detection. Hence, in this paper, Emperor Penguin Optimization-based Routing protocol (EPORP) is developed for detecting the Sybil attack which enhances the VANETs security. The main motive of the research is detecting the Sybil attack in VANETs for enhancing the secure operation. In the proposed approach, the Sybil attack will be detected with the help of the Rumour riding technique. To enhance the security of the VANETs, the Split XOR (SXOR) operation is utilized. In the SXOR operation, the optimal key is selected with the help of Emperor Penguin Optimization (EPO). The proposed method is implemented in the NS2 platform and performances are evaluated by metrics such as delay, throughput, delay, encryption time, and decryption time. The proposed method is compared with existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA) respectively. While analyzing the delivery ratio, the proposed method has 0.96 s, and the WOA, PSO, and FA are 0.94, 0.92, and 0.90 respectively. From the analysis, the proposed method has a high delivery ratio value compared with the WOA, PSO, and FA methods. Similarly, the other parameters are analyzed and compared with the existing methods.
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“Its performance is notably sensitive to dynamic VANET topologies, where irregular node density and fluctuating mobility patterns can lead to data fragmentation and diminished detection accuracy 16,18.”
From A Hybrid Deep Learning Model for Detection and Mitigation of DDoS Attacks in VANETs (2025) · cites this paper as [16]
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Cited by
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
Metadata
- Authors
- Velayudhan, N. C., Anitha, A., & Madanan, M.
- Venue
- Wireless Personal Communications
- Published
- 2021-09-24
- Volume
- 122
- Issue
- 4
- Pages
- 3573-3601
- ISSN
- 0929-6212
- 1572-834X
- Cited by
- 18
- References
- 0
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2026-03-09)
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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