The success of cryptocurrencies in transforming the digital financial landscape lies in the opportunity it brings to process payments (fast, borderless, and decentralized), but the same properties have contributed to the blistering pace of cybercrime evolution. This paper looks at the modes of illicit use of cryptocurrencies such as ransomware, darknet markets, money laundering and cross-chain obfuscation schemes and the extent to which these activities can be tracked through the use of forensic and analysis tools to navigate within the decentralized networks. The research operationalizes a mixed-method approach to mixer analysis that involves both analysis of high-profile illicit services with qualitative case studies of Tornado Cash, ChipMixer, and Bitcoin Fog and the quantitative analysis of graphs that use Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to perform the tasks of machine learning. Findings indicate that even though the most common laundering typologies are still the use of peel chains and mixer transactions, the novelties of cross-chain and DeFi-based obfuscation similar to mixer is the new frontier of illicit activity. GAT model reported the highest detection accuracy (F1-score 0.88), which supports the notion of the potential of AI-based blockchain forensics in matching suspicious addresses and clusters. More so, longitudinal research displays that the absolute values of illicit activity transactions are still increasing, and its percentage of overall crypto activities falls, meaning both an increasing market and greater enforcement capabilities. The findings underscore the contemporary necessity of interdisciplinary cooperation, instantaneous cross-chain monitoring, and proportionate regulatory approaches to ensure the cybercrime risks are restricted and legitimate privacy is maintained within the decentralized financial space.
Ayyubi et al. (2025) studied this question.