Cyber Threat Evaluation Using Neutrosophic Hyper Soft Rough Matrices: A TOPSIS-Based MCDM Method

Abstract

Cybersecurity monitoring systems continuously generate heterogeneous and uncertain alerts, making reliable threat prioritization a complex challenge. Traditional decision-making models are limited in their ability to quantify indeterminacy arising from dynamic attack conditions and incomplete security evidence. To address this issue, this paper proposes a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based Multi-criteria decision making (MCDM) method built on the Neutrosophic Hyper Soft Rough Matrix (NHSRM) framework. A MATLAB code is given to demonstrate the computational procedure of the proposed method, involving neutrosophic matrix formation, normalization, weighted aggregation, and closeness coefficient computation. Experimental outcomes indicate that attacks exploiting service disruption and unauthorized internal access pose greater operational impact compared to well-mitigated malware-based threats, reflecting the current maturity of defensive technologies in that domain. Sensitivity analysis highlights that inappropriate weight selection can lead to suboptimal threat ranking; therefore, periodic weight adjustment based on evolving attack intelligence is recommended.