Sybil Attack Prediction on Vehicle Network Using Deep Learning

  • Zulfahmi Helmi Universitas Syiah Kuala
  • Ramzi Adriman Universitas Syiah Kuala
  • Teuku Yuliar Arif Universitas Syiah Kuala
  • Hubbul Walidainy Universitas Syiah Kuala
  • Maya Fitria Universitas Syiah Kuala
Keywords: VANET, Intelligent Transportation System (ITS), Sybil Attack, Deep learning

Abstract

Vehicular Ad Hoc Network (VANET) or vehicle network is a technology developed for autonomous vehicles in Intelligent Transportation Systems (ITS). The communication system of VANET is using a wireless network that is potentially being attacked. The Sybil attack is one of the attacks that occur by broadcasting spurious information to the nodes in the network and could cause a crippled network. The Sybil strikes the network by camouflaging themselves as a node and providing false information to nearby nodes. This study is conducted to predict the Sybil attack by analyzing the attack pattern using a deep learning algorithm. The variables exerted in this research are time, location, and traffic density. By implementing a deep learning algorithm enacting the Sybil attack pattern and combining several variables, such as time, position, and traffic density, it reaches 94% of detected Sybil attacks.

 

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Published
2022-07-15
How to Cite
Zulfahmi Helmi, Ramzi Adriman, Teuku Yuliar Arif, Hubbul Walidainy, & Fitria, M. (2022). Sybil Attack Prediction on Vehicle Network Using Deep Learning. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(3), 499 - 504. https://doi.org/10.29207/resti.v6i3.4089
Section
Artikel Teknologi Informasi