Name
Abstract | ||
|---|---|---|
The current security method of Internet-of-Vehicles (IoV) systems is rare, which makes it vulnerable to various attacks. The malicious and unauthorized nodes can easily invade the IoV systems to destroy the integrity, availability, and confidentiality of information resources shared among vehicles. Indeed, access control mechanism can remedy this. However, as a static method, it cannot timely response to these attacks. To solve this problem, we propose an intelligent edge-chain-enabled access control framework with vehicle nodes and roadside units (RSUs) in this study. In our scenario, vehicle nodes act as lightweight nodes, whereas RUSs serve as full and edge nodes to provide access control services. Considering the low accuracy of risk prediction due to limited training sets, we leverage a generative adversarial networks (GANs) to convert the risk prediction to a sequence generation. Moreover, aiming at the problems of gradient disappearance and mode collapse existed in the original GANs, we devise a Wasserstein combined GANs (WCGANs). Simulation results demonstrate that WCGAN has higher prediction accuracy than the original GANs. Additionally, it can also improve the accuracy of access control of risk prediction-based access control (RPBAC) model. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JIOT.2021.3061467 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Access control,blockchain,edge computing (EC),generative adversarial networks (GANs),Internet of Vehicles (IoV) | Journal | 8 |
Issue | ISSN | Citations |
15 | 2327-4662 | 2 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanni Liu | 1 | 4 | 2.44 |
| Man Xiao | 2 | 2 | 0.69 |
| Shanzhi Chen | 3 | 889 | 66.76 |
| Fan Bai | 4 | 89 | 12.70 |
| Jianli Pan | 5 | 471 | 33.61 |
| Di Zhang | 6 | 64 | 20.01 |