Projection measure-driven optimization of q-rung orthopair fuzzy MAGDM for computer network security evaluation

Abstract

The computer network environment is very complex, and there are many factors that need to be considered in the process of network security evaluation. At the same time, various factors have complex nonlinear relationships. Neural networks are mathematical models that simulate the behavioral characteristics of animal neural networks. They process information by adjusting the connection relationships of internal nodes, and have a wide range of applications in solving complex nonlinear relationship problems. The computer network security evaluation is multiple attribute group decision making (MAGDM) problems. In this paper, based on projection measure and bidirectional projection measure, we shall introduce four forms projection models with q-rung orthopair fuzzy sets (q-ROFSs). Furthermore, combine projection measure and bidirectional projection measure with q-ROFSs, we develop four forms of projection models with q-ROFSs. Based on developed weighted projection measure models, the multiple attribute group decision making (MAGDM) model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons are also conducted to verify advantages of the new built methods.