Graph Based Visualisation Techniques for Analysis of Blockchain Transactions

Tharani, Jeyakumar Samantha; Charles, E.Y.A.; Hóu, Zhé; Palaniswami, Marimuthu; Muthukkumarasamy, Vallipuram

doi:10.1109/lcn52139.2021.9524878

Cited by 19 publications

(7 citation statements)

References 7 publications

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“…The graph-based feature extraction models the transaction JSONs as graphs. The graph modelling involves various algorithms [19]. Different algorithms are needed for a different type of blockchain network (Unspent Transaction Output (UTXO) or account-based).…”

Section: Overview Of the Proposed Methodologymentioning

confidence: 99%

Feature Engineering for Anomaly Detection and Classification of Blockchain Transactions

Jeyakumar¹,

Yugarajah²,

Rathore³

et al. 2023

Preprint

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<p>The study analysed the importance of blockchain transaction features to identify suspicious activities. The feature engineering process involves exploiting domain knowledge, applying intuition, and performing a time-consuming series of trial-and-error extractions. Manually overseeing this process significantly impacts the performance of model generation. We address this challenge with an automated feature engineering approach to extract the various features from blockchain transactions. Also, we engineered a set of new features based on statistical measures and graph representation. We demonstrate that the proposed approach can be applied to various blockchain transaction datasets, including Bitcoin and Ethereum. The engineered features were tested against eight classifiers, including random forest, XG-boost, Silas, and neural network-based classifiers to identify the suspicious behaviour of transactions</p>

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Section: Overview Of the Proposed Methodologymentioning

confidence: 99%

Feature Engineering for Anomaly Detection and Classification of Blockchain Transactions

Jeyakumar¹,

Yugarajah²,

Rathore³

et al. 2023

Preprint

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“…The Algorithms 1, 2, and 3 are extended from the work published in [19] by adding the node's properties.…”

Section: ) Hypergraphmentioning

confidence: 99%

Visualizing Blockchain Transaction Behavioural Pattern: A Graph-based Approach

Jeyakumar¹,

Hóu²,

Yugarajah³

et al. 2023

Preprint

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<p>This paper proposes a novel graph-based visualisation approach to incorporating automated graph modelling and generalised graph algorithms from blockchain transactions. Our approach enables users to interact directly with the blockchain data using graph queries and provides exploration capabilities through graph patterns. Four case studies are presented, demonstrating the benefits of the proposed approach in identifying the behaviour of anomalous nodes. The visual assessment of behavioural patterns informs the challenges in classifying anomalous nodes. The proposed approach also has the potential to be used in other types of networks for inferring and verifying heuristics.</p>

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“…Graph visualization is used to handle the complexity of sequences of transactions. Different kinds of graphs and representations can be used to fit certain needs [52][53][54]. However, in the majority of cases, it is desirable to present the flow as a graph, in which addresses are represented as nodes and transactions as directed edges (Fig.…”

Section: Flow Comprehensionmentioning

confidence: 99%

Blockchain transaction analysis: a comprehensive review of applications, tasks and methods

Dorogyy,

Kolisnichenko

2023

SRIT

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Blockchain transaction analysis is a powerful tool to gain insights into the actions and conduct of participants within blockchain networks. This article aims to extensively examine the applications, tasks, and methods associated with blockchain transaction analysis. We look at various uses of transaction analysis, ranging from its instrumental role in blockchain development to its pivotal significance in the field of criminal investigations. By leveraging common techniques and technologies employed in conducting such an analysis, we unlock hidden insights and uncover information that is not visible at first look. This article offers a wide-ranging perspective on the profound significance of blockchain transaction analysis while shedding light on its key role within the cryptocurrency industry and its wide-ranging applications beyond.

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Graph Based Visualisation Techniques for Analysis of Blockchain Transactions

Cited by 19 publications

References 7 publications

Feature Engineering for Anomaly Detection and Classification of Blockchain Transactions

Feature Engineering for Anomaly Detection and Classification of Blockchain Transactions

Visualizing Blockchain Transaction Behavioural Pattern: A Graph-based Approach

Blockchain transaction analysis: a comprehensive review of applications, tasks and methods

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