LAUSR

Device-to-device (D2D) communications can effectively offload the traffic of cellular system in a distributed way. However, during the data forwarding process, malicious D2D users can intermittently discard data of other users, which seriously affects the data forwarding efficiency. Therefore, a malicious-forwarding-behavior-aware link selection mechanism (MBLS) is proposed in this paper to alleviate the influence of malicious attacks. User behaviors are analyzed according to the correlation between the social relationship and forwarding behavior of users, and the identification of malicious behavior is obtained by Elman neural network. Thus, malicious users can be detected, and then the optimal link can be selected. The simulation results show that the proposed mechanism can effectively detect the malicious behaviors of D2D users, notably improve the reliability of data transmission and significantly enhance the network performance.