Enhancing Cybersecurity Risk Assessment Techniques Based on Bipolar Complex Intuitionistic Fuzzy Soft Robust Aggregation Operators: A Comprehensive Approaches

Abstract

This article describes the cybersecurity risk assessment model’s accuracy, timely response, and actions against potential threats by providing the key areas in which the cybersecurity risk assessment model can be improved and enhanced. For this, we propose the model of robust aggregation operators based on bipolar complex intuitionistic fuzzy soft information, called the bipolar complex intuitionistic fuzzy soft weighted averaging operator, bipolar complex intuitionistic fuzzy soft order weighted averaging operator, bipolar complex intuitionistic fuzzy soft weighted geometric operators, and bipolar complex intuitionistic fuzzy soft order weighted geometric operators, with some practical properties, called idempotency, monotonicity, and boundedness. Further, we discuss the multi-attribute decision-making model based on proposed operators for evaluating the enhancement of the cybersecurity risk assessment techniques with a comprehensive approach. Finally, we illustrate numerical examples based on decision-making techniques for assessing the comparative analysis among the proposed ranking values with existing ranking values to show the worth and verity of the designed models.