Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Vol 6 No 3 (2022): Juni 2022

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)



Article Info

Publish Date
15 Jul 2022

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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Journal Info

Abbrev

RESTI

Publisher

Subject

Computer Science & IT Engineering

Description

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat ...