Cryptocurrencies Forensics With Real-Time intelligence and Graph Database: a Comprehensive Review

The advent of cryptocurrencies has paved the way for an alternative financial system based on blockchain technology. The properties of this technology allow untrustworthy entities to exchange transactions with each other, confidentially, transparently and anonymously. This latter property combined to the solutions for mixing cryptocurrency transactions has contributed significantly to strengthening the anonymization of cryptocurrencies. Consequently, they have facilitated illegal activities. Nowadays, the majority of financial crimes are carried out using cryptocurrencies. To mitigate this problem, cryptocurrencies forensics techniques have been suggested with the aim of reducing illicit financial activities. However, most of these existing approaches do not support real-time processing, do not offer architecture for Big Data processing. Moreover, these methods do not provide an optimized data storage system based on a graph database. This paper performs a comprehensive investigation into cryptocurrencies forensics techniques including real-time big data processing and graph database storage. It explores and classifies existing cryptocurrencies forensics techniques to effectively support decision making for their implementation. Furthermore, it reviews recent research effort to enforce graph data storage system, big data and real-time processing while executing cryptocurrencies forensics. Through this paper, we aim to clear up the outlook by suggesting an outline of the different method, tools and application when raising the basic differences and presenting associated advantages and limitations. More importantly, we underline current challenges and future research directions to stimulate research in cryptocurrencies forensics with real-time and big data processing using graph data storage.