Information Security Risk Assessment in Industry Information System Based on Fuzzy Set Theory and Artificial Neural Network

Abstract

Information security risk assessment is a crucial component of industrial management techniques that aids in identifying, quantifying, and evaluating risks in comparison to criteria for risk acceptance and organizationally pertinent objectives. Due to its capacity to combine several parameters to determine an overall risk, the traditional fuzzy-rule-based risk assessment technique has been used in numerous industries. The technique has a drawback because it is used in situations where there are several parameters that need to be evaluated, and each parameter is expressed by a different set of linguistic phrases. In this paper, fuzzy set theory and an artificial neural network (ANN) risk prediction model that can solve the issue at hand are provided. Also developed is an algorithm that may change the risk-related factors and the overall risk level from a fuzzy property to a crisp-valued attribute is developed. The system was trained by using twelve samples representing 70%, 15%, and 15% of the dataset for training, testing, and validation, respectively. In addition, a stepwise regression model has also been designed, and its results are compared with the results of ANN. In terms of overall efficiency, the ANN model (R2= 0.99981, RMSE=0.00288, and MSE=0.00001,) performed better, though both models are satisfactory enough. It is concluded that a risk-predicting ANN model can produce accurate results as long as the training data accounts for all conceivable conditions.