Adaptive Trust Evaluation Model Based on Entropy Weight Method for Sensing Terminal Process.

Remote sensing (RS) has been widely used for data acquisition, monitoring, control, and intelligent decision-making. However, most of them are unattended and easily become the target of attack, which means there are still some risks in the sensing terminal processes. Therefore, trust evaluation of the processes associated with the sensing terminal is necessary. The existing trust evaluation model based on the sensing terminal process has some defects, such as low performance, low precision, and difficulty in effectively identifying malicious processes in the sensing terminal. In this paper, an adaptive trust evaluation model based on the entropy weight method is proposed to detect the sensing terminal process (PB-ATEM). By establishing two kinds of trust values, the direct trust value and the reciprocal trust value, we can comprehensively judge whether the process is trustworthy. For the direct trust value, we can dynamically capture the auto-correlation value of processes and establish a dynamic reward and punishment function to improve the response to malicious processes. For reciprocal trust values, k-means cluster analysis is used to classify processes, and the optimal entropy weight method is proposed to calculate the direct trust weight and reciprocal trust weight of each process more accurately, which accelerates the exposure of malicious processes. Finally, these two trust values are weighted to obtain the integrated trust value. The simulation results show that PB-ATEM can respond quickly to malicious processes. Compared with the existing trust evaluation models, it has higher detection accuracy and better ability to identify malicious processes.