Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CCNC46108.2020.9045356 | 2020 IEEE 17TH ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC 2020) |
Keywords | DocType | ISSN |
VANET, V2V, sybil attack, ADAS sensors | Conference | 2331-9852 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kiho Lim | 1 | 0 | 0.34 |
| Tariqul Islam | 2 | 0 | 0.34 |
| Hyunbum Kim | 3 | 61 | 11.57 |
| Jingon Joung | 4 | 2 | 1.75 |