A reputation mechanism based Deep Reinforcement Learning and blockchain to suppress selfish node attack motivation in Vehicular Ad-Hoc Network

【Author】 Zhang, Bowei; Wang, Xiaoliang; Xie, Ru; Li, Chuncao; Zhang, Huazheng; Jiang, Frank

【Source】FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

【影响因子】7.307

【Abstract】The selfish On-Board-Unit (OBU) attacks Vehicular Ad-Hoc Network (VANET) by various attacks for profit. However, many existing methods are based on the principle of direct reciprocity for communication, which makes it easy for large-scale networks to crash when an attack occurs. In order to reduce the number of attackers in the vehicle ad hoc network and restrain the attack motivation of the OBUs, we propose an indirect reciprocal incentive mechanism based on reputation to encourage the OBUs in the VANET to help each other. Since most OBUs are in great need of network services, including potential attackers, when the loss of network services is far greater than the illegal benefits of their attacks, selfish and rational OBUs will give up attacks and take desirable behavior. In addition, to prevent some attacks from tampering with information, we also apply blockchain technology to record the behavior of OBU. The indirect reciprocity process of each OBU in VANET can be regarded as Markov Decision Process (MDP). In order to restrain the attack motivation of selfish nodes and communicate normally without knowing the attack model, an algorithm based on Deep Reinforcement Learning (DRL) is proposed to suppress attack motivation, so as to activate OBU learning in a dynamic environment and make wise decisions. Finally, through a large number of simulation experiments, the performance of our proposed algorithm is obviously better than that of the baseline strategy, and is verified by the simulation results.(c) 2022 Elsevier B.V. All rights reserved.

【Keywords】Blockchain; Deep reinforcement learning; Indirect reciprocity; Security; Vehicular Ad-Hoc network; Vehicle reputation

【发表时间】2023 FEB

【收录时间】2022-11-03

【文献类型】实证数据

【主题类别】

区块链应用-实体经济-交通领域