Topographic Attribute Map-Based Visualisation to Uncover Behaviour Patterns in Blockchain Networks

Blockchain and its derived technologies lead the future generation to a trust-based digital world. Being decentralised, immutable, and transparent has offered integrity, availability, authenticity, and non-repudiation for shared assets of the agricultural, medical, education and financial sectors. Pseudo-anonymity is one of the crucial properties of the blockchain network that preserves the privacy of the participants by hiding their original information. However, this feature is being exploited via receiving illegal gains such as ransomware settlements, Ponzi schemes, scams, phishing and dark market trades in the form of crypto assets. These rising crimes have become a challenge for law enforcement authorities, forensic analysts, and financial authorities, necessitating the development of more sophisticated detection methods. Understanding the mechanisms used by criminals requires domain experts’ knowledge. Further, as the native structure of the blockchain data is complex and large in volume, this raises a challenge in analysis. This paper proposes a methodology to use Topographic Attribute Maps (TAM), a novel data visualisation method for visualising tactics behind illegal activities based on the layout of the transaction network and the characteristics of the transactions. The results show that the proposed visualisation technique enriches the existing attribute-based graph as digital evidence, via classifying with contour lines and colour maps, positioning the accounts in the network as nodes, and representing the links with other accounts. These features of TAM are comprehensible for professionals from other domains, especially those from non-technical backgrounds.