GuiltyWalker: Distance to illicit nodes in the Bitcoin network

Oliveira, Catarina R.; Torres, João Paulo Machado; Silva, Maria Inês; Aparício, David; Ascensão, João Tiago; Bizarro, Pedro

doi:10.48550/arxiv.2102.05373

Cited by 3 publications

(4 citation statements)

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“…To construct graph features on the data, the authors treat accounts as nodes and transactions as directed edges. Results indicate that the inclusion of Guilty-Walker features [81], using random walks to capture the distances between a given node and illicit nodes, increases model performance.…”

Section: A Unsupervised Suspicious Behavior Flaggingmentioning

confidence: 99%

Fighting Money Laundering With Statistics and Machine Learning

Jensen

Iosifidis²

2023

IEEE Access

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Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this paper, we focus on anti-money laundering in banks and provide an introduction and review of the literature. We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging. We find that client risk profiling is characterized by diagnostics, i.e., efforts to find and explain risk factors. On the other hand, suspicious behavior flagging is characterized by non-disclosed features and hand-crafted risk indices. Finally, we discuss directions for future research. One major challenge is the need for more public data sets. This may potentially be addressed by synthetic data generation. Other possible research directions include semi-supervised and deep learning, interpretability, and fairness of the results.INDEX TERMS Anti-money laundering, know-your-client, machine learning, literature review.

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Section: A Unsupervised Suspicious Behavior Flaggingmentioning

confidence: 99%

Fighting Money Laundering With Statistics and Machine Learning

Jensen

Iosifidis²

2023

IEEE Access

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“…Lorenz et al [18] proposed active learning techniques to study the minimum number of tags necessary to achieve high detection and test their illegal activity datasets. In addition, Alarab et al [19] presented an integrated learning method using a given combination of supervised learning models and applied it to the elliptic dataset, improving the baseline results. Jensen et al [20] used gated recurrent units and a self-attention model, which can reduce the number of false positives when qualifying alarms.…”

Section: Related Workmentioning

confidence: 99%

HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering

Song,

2023

Applied Sciences

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In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions.

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“…that were used in their classification model as exchange addresses that are possibly involved in money laundering/Bitcoin mixing. Oliveira et al (2021) proposed GuiltyWalker, a method that performs random walks on a Bitcoin address network and extracts features based on the distance from illicit nodes. Subsequently, they used the graph features along with other static ones to classify the addresses as illicit or not.…”

Section: Related Workmentioning

confidence: 99%

“…Such approaches typically analyse Bitcoin transactions by extracting several features aiming to determine whether they are related to criminal actions. The majority of existing methods employ classification models to infer whether an address is involved in illicit activities by extracting static features, i.e., without considering the evolution over time (Oliveira et al 2021;Ranshous et al 2017;Toyoda et al 2017;Yang et al 2022). In particular, most commonly, the whole timeline of transactions related to an address is summarised into static features; for example, the transaction volume of an address over the whole activity duration is typically summarised into a single value (Farrugia et al 2020;Lin et al 2019;Toyoda et al 2017Toyoda et al , 2018aToyoda et al , 2019.…”

Section: Introductionmentioning

confidence: 99%

Change Point Analysis of Time Series Related to Bitcoin Transactions: Towards the Detection of Illegal Activities

Theodosiadou,

Koufakis,

Tsikrika

et al. 2023

JRFM

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This paper proposes a unified framework for the detection of statistically significant changes in time series related to Bitcoin transactions. The time locations of these changes are linked to the occurrences of events which could be further investigated aiming to reveal potential illicit activity. The proposed framework includes: (a) the extraction of 28 features of interest in the form of time series from the Bitcoin transaction history; (b) the selection of features among the extracted ones based on the Partition Around Medoids clustering approach; and (c) the change point analysis of the multivariate time series which is formulated by the medoid time series of each cluster. This analysis enables the identification of structural breaks in the underlying behavior of the time series of interest at certain time points. The proposed framework is applied on the Bitcoin transactions of two entities that have been involved in illicit activities, namely Pirate@40, who orchestrated a high-yield investment programme, and the MintPal Bitcoin exchange platform that was hacked. The analysis results indicate that the estimated change points can be linked to certain event occurrences which may affect the transaction activity and could be further investigated for potential links to illicit actions.

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GuiltyWalker: Distance to illicit nodes in the Bitcoin network

Cited by 3 publications

References 0 publications

Fighting Money Laundering With Statistics and Machine Learning

Fighting Money Laundering With Statistics and Machine Learning

HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering

Change Point Analysis of Time Series Related to Bitcoin Transactions: Towards the Detection of Illegal Activities

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