Developing a Cryptocurrency Susceptibility Test: Mathematical Modeling and Benchmarking

Abstract

Abstract The increasing prominence of cryptocurrencies has brought to the forefront the critical issue of security vulnerabilities, particularly the majority 51% attack. This study addresses the need for benchmarks to distinguish between vulnerable and non-vulnerable cryptocurrencies. A comprehensive literature review reveals a lack of research with the desired statistical rigor in this domain, necessitating the development of a robust model. Drawing upon mathematical modeling, this research fills the gap by proposing a susceptibility test model which incorporates essential parameters identified from literature. The model is validated with additional data to ensure its accuracy and reliability. Furthermore, k-means clustering analysis is employed to determine benchmarking thresholds, allowing for a refined categorization of cryptocurrencies based on their susceptibility levels. The findings of this study reveal five distinct clusters, each representing a unique security profile. Resilience is associated with susceptibility test values less than the critical threshold \(0.532\). In contrast, cryptocurrencies with susceptibility test values greater than 1.557 exhibit alarming vulnerability. In between, three cryptocurrency susceptibility levels are identified, ranging from moderate resilience to high vulnerability. The outcomes of this study serve as a foundation for better-informed investment decisions as well as future research endeavors, informing the development of industry best practices and policy recommendations aimed at strengthening the robustness of cryptocurrencies against malicious activities.