@inproceedings{39ec569628004fc5bdc530f41fa166e8,
title = "A Sybil Attack Detection Scheme based on ADAS Sensors for Vehicular Networks",
abstract = "Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack detection scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object detection technique is used to accurately identify nearby objects for Sybil attack detection and the multi-step verification process minimizes the false positive of the detection.",
keywords = "ADAS sensors, V2V, VANET, sybil attack",
author = "Kiho Lim and Tariqul Islam and Hyunbum Kim and Jingon Joung",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 17th IEEE Annual Consumer Communications and Networking Conference, CCNC 2020 ; Conference date: 10-01-2020 Through 13-01-2020",
year = "2020",
month = jan,
doi = "10.1109/CCNC46108.2020.9045356",
language = "English (US)",
series = "2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020",
}