| Title | A Sybil Attack Detection Scheme based on ADAS Sensors for Vehicular Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Lim, K., Islam, T., Kim, H., Joung, J. |
| Conference Name | 2020 IEEE 17th Annual Consumer Communications Networking Conference (CCNC) |
| Keywords | ADAS sensors, Advanced Driving Assistant System, autonomous driving, composability, deep learning based object detection technique, driver information systems, Global Positioning System, Laser radar, learning (artificial intelligence), Metrics, multiple forged identities, multistep verification process, object detection, pubcrawl, Resiliency, road safety, safety-related services, Sensor systems, Sensors, Sybil attack, Sybil attack detection scheme, sybil attacks, telecommunication computing, telecommunication security, Trajectory, trusted third party authorities, V2V, VANET, vehicular ad hoc network, vehicular ad hoc networks, vehicular communications |
| 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. |
| DOI | 10.1109/CCNC46108.2020.9045356 |
| Citation Key | lim_sybil_2020 |
A Sybil Attack Detection Scheme based on ADAS Sensors for Vehicular Networks