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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Zulfahmi Helmi
Universitas Syiah Kuala
Computer Science & IT Engineering

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Sybil Attack Prediction on Vehicle Network Using Deep Learning Zulfahmi Helmi; Ramzi Adriman; Teuku Yuliar Arif; Hubbul Walidainy; Maya Fitria
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (434.315 KB) | DOI: 10.29207/resti.v6i3.4089

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.
Co-Authors Adriman, Ramzi Ali Djamhuri Hubbul Walidainy Maya Fitria Teuku Yuliar Arif