LLM-Assisted AHP for Explainable Cyber Range Evaluation

TL;DR

This paper introduces an LLM-assisted AHP framework for objectively evaluating Cyber Ranges, focusing on critical infrastructure training, by quantifying multiple criteria to improve platform assessment and comparison.

Contribution

It presents a novel evaluation framework using LLM-supported AHP to provide standardized, reproducible, and explainable assessments of Cyber Range platforms.

Findings

Framework produces quantitative scores for CR comparison

LLM-assisted pairwise comparisons ensure consistency

Facilitates objective evaluation and improvement insights

Abstract

Cyber Ranges (CRs) have emerged as prominent platforms for cybersecurity training and education, especially for Critical Infrastructure (CI) sectors that face rising cyber threats. One way to address these threats is through hands-on exercises that bridge IT and OT domains to improve defensive readiness. However, consistently evaluating whether a CR platform is suitable and effective remains a challenge. This paper proposes an evaluation framework for CRs, emphasizing mission-critical settings by using a multi-criteria decision-making approach. We define a set of evaluation criteria that capture technical fidelity, training and assessment capabilities, scalability, usability, and other relevant factors. To weight and aggregate these criteria, we employ the Analytic Hierarchy Process (AHP), supported by a simulated panel of multidisciplinary experts implemented through a Large Language Model (LLM). This LLM-assisted expert reasoning enables consistent and reproducible pairwise comparisons across criteria without requiring direct expert convening. The framework's output equals quantitative scores that facilitate objective comparison of CR platforms and highlight areas for improvement. Overall, this work lays the foundation for a standardized and explainable evaluation methodology to guide both providers and end-users of CRs.