Risk-Based Access Control Mechanism for Internet of Vehicles Using Artificial Intelligence

【Author】 Priscila, S. Silvia; Sharma, Abhishek; Vanithamani, S.; Ahmad, Faiyaz; Mahaveerakannan, R.; Alrubaie, Ali Jawad; Jagota, Vishal; Singh, Bhupesh Kumar

【Source】SECURITY AND COMMUNICATION NETWORKS

【影响因子】1.968

【Abstract】Internet of Vehicles (IoV) systems are vulnerable to a wide range of attacks because of the lack of security measures. IoV systems can be infiltrated by malicious and unauthorized nodes, which can cause the authenticity, accessibility, and privacy of shared information resources to be compromised. Indeed, the use of an access control system can help; as a result, it is unable to respond to such attacks on time. This paper introduces an artificial intelligence-enabled access control mechanism (AI-ACM) with vehicle nodes and roadside units (RSUs) to overcome these issues. Here, use vehicle nodes as lightweight nodes, while RSUs act as comprehensive and edge nodes to provide access control service. A generative adversarial network (GAN) is used in place of risk prediction (RP) due to the lack of training sets, resulting in a sequence generation rather than an accurate risk prediction. Afterward, the blockchain-based Internet of Vehicles (BIoV) approach is summarized for the security mechanisms of vehicles that are discussed from the aspects of access control and authentication to sustain the distributed processing architecture and solve security issues. The simulation results show that AI-ACM is more accurate than the previous GANs at predicting the future. In addition, the RP model's access control accuracy can be improved as a result of this technique.

【Keywords】

【发表时间】2022 44789

【收录时间】2022-09-15

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-物联网