Enhancing multi-criteria decision-making in blockchain security: a hybrid machine learning and PROMETHEE approach

Abstract

Multi-Criteria Decision-Making (MCDM) techniques play a critical role in solving complex decision problems involving multiple conflicting criteria across various domains. The Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) is a widely adopted MCDM method known for its structured and transparent ranking process. However, conventional PROMETHEE implementations often rely on manually assigned criteria weights, introducing subjectivity and inconsistency in decision-making. This study proposes a novel hybrid approach that integrates Machine Learning (ML) techniques with PROMETHEE to enhance decision-making, particularly in blockchain security risk assessment. The proposed ML-PROMETHEE model leverages ML algorithms for feature selection, weight assignment, and preference function optimization, ensuring an automated and data-driven ranking process that minimizes human bias and enhances decision reliability. A real-world case study in blockchain security risk assessment is conducted to validate the proposed methodology. This study employs ML models such as Random Forest (RF) for feature selection and SHapley Additive exPlanations (SHAP) analysis to determine the most influential security risk factors. Additionally, Artificial Neural Networks (ANNs) and eXtreme Gradient Boosting (XGBoost) are used to optimize weight assignment, refining the decision-making process before integrating it into the PROMETHEE framework. Experimental results demonstrate that the ML-enhanced PROMETHEE model achieves a 17.3% improvement in decision accuracy, a 43.5% reduction in execution time, and a 40.2% reduction in subjective bias compared to traditional PROMETHEE-based decision-making. These findings highlight the potential of ML-enhanced MCDM models in strengthening the robustness, transparency, and scalability of decision frameworks, particularly in rapidly evolving domains such as blockchain security. Future research will explore the integration of deep learning and reinforcement learning techniques to further enhance automated decision-making frameworks.