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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
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| Paper Title: | Securing VANET from Sybil Attack using ECC, DSA, SAD and Detecting using Hybrid Model |
| Authors Name: | K MADHUSHRI , Dr.R.Arockia Xavier Annie |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_208705
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| Published Paper Id: | IJRTI2512134 |
| Published In: | Volume 10 Issue 12, December-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i12.208705 |
| Abstract: | In VANETs keeping communications safe matters for transport systems to work well. The Sybil attack may cause many problems, such as spreading false traffic details, unauthorized use of important services and even car crashes. This study tries to build a strong way to spot such attacks by using the Elliptic Curve Cryptosystem, Digital Signature Algorithm and Sybil Attack Detection (SAD) Algorithm to reduce this risk. The main aim is to design a system to handle Sybil attacks in VANET. The Elliptic Curve Cryptosystem will make separate identities for each vehicle and the Digital Signature Algorithm will mark messages moreover check if they are real. Detect Sybil attack from VANET Dataset using hybrid model. We propose a method that pairs CNNs with a pre-trained VGG16 architecture to catch Sybil attacks in VANETs. The approach uses a set of features that covers vehicle ID, fake messages, geographical ID, attack type along with attack source to identify Sybil assaults in the network. VGG16 and transfer learning take advantage of the pre-trained model's ability to show deep details, which helps with both feature extraction moreover classification. The procedure helps adjust model settings to improve training accuracy and lower loss. To speed up learning moreover boost performance, it uses Particle Swarm Optimizer (PSO) to change network weights. Testing shows that the combined CNN-VGG16 and PSO method can reliably spot Sybil attacks in VANETs, which strengthens the security and reliability of vehicle communication systems. |
| Keywords: | VANET,ECC,DSA,SAD,VGG16,CNN,PSO,Sybil, Omnet IDE |
| Cite Article: | "Securing VANET from Sybil Attack using ECC, DSA, SAD and Detecting using Hybrid Model", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.b254-b261, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512134.pdf |
| Downloads: | 000147 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2512134
Registration ID:208705
Published In: Volume 10 Issue 12, December-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i12.208705
Page No: b254-b261 Country: Chennai , Tamilnadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2512134 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2512134 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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