Quantitative analysis of cryptocurrency susceptibility: A mathematical benchmarking model

Abstract

The rise of cryptocurrencies has brought attention to significant security challenges, particularly the 51% attack. This study focuses on developing benchmarks to evaluate varying levels of vul nerability among cryptocurrencies. A detailed review of the literature identifies a lack of ap proaches having statistical rigor, leading to the development of a comprehensive susceptibility test model. The proposed model is basedonkey parameters extracted from existing studies and validated with additional quantitative data for accuracy and reliability. Benchmarking thresholds are determined using k-means clustering, allowing for the classification of cryptocurrencies into distinct security profiles. The analysis identifies five clusters: resilient cryptocurrencies have sus ceptibility scores below 0.532, while scores exceeding 1.557 indicate high vulnerability. The re maining clusters represent intermediate levels of resilience and risk. These findings contribute to a better understanding of cryptocurrency security, supporting informed investment decisions and providing a basis for future research and policy development.