Jurnal RESTI: Rekayasa Sistem dan Teknologi Informasi | |
Sybil Attack Prediction on Vehicle Network Using Deep Learning | |
article | |
Zulfahmi Helmi1  Ramzi Adriman1  Teuku Yuliar Arif1  Hubbul Walidainy1  Maya Fitria1  | |
[1] Universitas Syiah Kuala | |
关键词: VANET; Intelligent Transportation System (ITS); Sybil Attack; Deep learning; | |
DOI : 10.29207/resti.v6i3.4089 | |
来源: Ikatan Ahli Indormatika Indonesia | |
【 摘 要 】
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.
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
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RO202307110004181ZK.pdf | 424KB | download |