BitAnalysis: A Visualization System for Bitcoin Wallet Investigation

Sun, Yujing; Xiong, Hao; Yiu, Siu Ming; Lam, Kwok-Yan

doi:10.1109/tbdata.2022.3188660

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…For surmounting the intricacies of cryptocurrency probes, reliance on specialized blockchain analytical tools has been observed [33]. Such tools have been reported to facilitate the tracing of transactions and the identification of involved parties, streamlining the case-building process against entities embroiled in malfeasance [34,35].…”

Section: Emphasis On Blockchain Analytical Toolsmentioning

confidence: 99%

Cryptocurrency Investigations in Digital Forensics: Contemporary Challenges and Methodological Advances

Raza,

Shaikh,

Tahira

2023

IDA

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Digital forensics, a crucial subset of cybersecurity, encompasses sophisticated tools and methodologies for the interpretation, analysis, and investigation of digital evidence, facilitating the identification and mitigation of cybercrimes and security breaches. With the advent of cryptocurrencies, an array of unique challenges has emerged in the domain of digital forensic investigations. This review elucidates the prevailing state of digital forensic practices vis-à-vis cryptocurrencies, emphasizing the obstacles and limitations inherent in probing decentralized and intricate technologies. Notable deficiencies in extant investigative practices were observed. Solutions proffered encompass the formulation of novel software applications tailored for cryptocurrency analyses, the integration of machine learning and artificial intelligence capabilities, and the employment of advanced analytics to discern patterns and irregularities within blockchain transactions. Furthermore, a pioneering methodology, merging traditional digital forensic strategies with blockchain-specific techniques, is posited for efficacious cryptocurrency inquiries. The analysis underscores the imperative for a renewed paradigm in digital forensic examinations to surmount the challenges integral to cryptocurrency probes. By forging novel methodologies and standardizing investigative procedures, support for legal enforcement endeavors can be enhanced, facilitating the efficacious detection and prosecution of cryptocurrency-associated misdemeanors.

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Section: Emphasis On Blockchain Analytical Toolsmentioning

confidence: 99%

Cryptocurrency Investigations in Digital Forensics: Contemporary Challenges and Methodological Advances

Raza,

Shaikh,

Tahira

2023

IDA

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“…These exchanges hold the assets and transaction records of users and possess certain supervisory and intervention capabilities. Thirdly, Bitcoin has a concentration of holders, with a relatively small number of individuals holding a significant amount of Bitcoin [12,13]. The trading activities and holdings of these holders have a certain level of influence [14], leading to market fluctuations and price changes.…”

Section: Introductionmentioning

confidence: 99%

Statistical and clustering analysis of attributes of Bitcoin backbone nodes

Xu,

Gao,

Zhu

et al. 2023

PLoS ONE

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Bitcoin is a decentralized digital cryptocurrency. Its network is a Peer-to-peer(P2P) network consisting of distributed nodes. Some of these nodes are always online and in this article are called Bitcoin backbone nodes. They have a significant impact on the stability and security of the Bitcoin network, so it is meaningful to analyze and discuss them. In this paper, we first continuously collect information about Bitcoin nodes from July 2021 through June 2022 (which is the longest duration of data collection to date). In total, we collect information on 127,613 Bitcoin nodes. At the same time, we conclude that the fluctuation of Bitcoin nodes is directly related to the fluctuation of onion network nodes. Further, we filtered 2694 Bitcoin backbone nodes based on our algorithm. By analyzing the backbone nodes’ attributes such as geographic distribution, client version, operator, node function, and abnormal port number, it is demonstrated that these nodes are centralized and play an important role in the Bitcoin network. Based on this, three unsupervised machine learning algorithms are selected to cluster multiple attributes of backbone nodes in a more scientific way. In this paper, the whole process from data collection to cluster analysis is completed and the best results are obtained by comparison. The experiments proved the existence of centralization of Bitcoin backbone nodes and obtained the number of nodes within each cluster. Finally, cluster nodes are de-anonymized based on the optimal results. Through our experiments, we obtain organizational information about the deployers of 103 nodes, linking the Bitcoin backbone nodes to the real world, thus accurately demonstrating the existence of Bitcoin centrality.

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