Bitcoin Mixing Detection Using Deep Autoencoder

Nan, Lihao; Tao, Dacheng

doi:10.1109/dsc.2018.00047

Cited by 29 publications

(19 citation statements)

References 6 publications

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“…Some swapping service providers, such as Darklaunder, repeatedly use a single receiving address in a short time [140], resulting in a larger degree of centrality than normal nodes [138]. Moreover, since users who belong to different communities may use the same mixing service, mixing service nodes can also act as bridges to nodes with few connections before mixings [141].…”

Section: Mixing Serivcesmentioning

confidence: 99%

Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.

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Section: Mixing Serivcesmentioning

confidence: 99%

Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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“…Identification of abnormal activities within Bitcoin transactions has been intensively addressed in the past. Such works addressed a broad range of security issues and challenges, including, but not limited to, privacy and deanonymization investigation, 4 botnets detection, 5 abnormal and fraudulent transactions detection, 6,7 malicious users and miners detection, [8][9][10][11] darknet markets, 12 money laundering and drug trading, 13 etc. However, it is worth noting that feature engineering (ie, which comprises various mechanisms and approaches including network embeddings, clustering, and network traffic characterization) is a vital and crucial aspect of most of Bitcoin-based research works that mainly focus on illegitimate user and miner activity identification.…”

Section: Related Workmentioning

confidence: 99%

Anomaly detection in blockchain using network representation and machine learning

Martin

Rahouti

Ayyash

et al. 2021

Security and Privacy

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The vast majority of digital currency transactions rely on a blockchain framework to ensure quick and accurate execution. As such, understanding how a blockchain works is vital to understanding the dynamics of cryptocurrency operations. One of the key benefits of this type of system is the exhaustive records captured in a given marketplace. The interwoven movement between agents can effectively be expressed as a graph via the extraction of historical data from the blockchain. By looking at a specific blockchain as an interaction of its agents, network representation learning can be leveraged to examine these relationships. Furthermore, the analysis of a graph structure can be enhanced through the application of modern and sophisticated machine learning techniques. Leveraging the automated nature of these methods can create meaningful observations of the input network. In this paper, we utilize several machine learning models to detect anomalous transactions in various digital currency markets. We find that supervised learning techniques yield encouraging results, whereas unsupervised learning techniques struggle more with the classification.

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“…Other illicit users are ransom calls for extortions, bombs threats, sextortionists, scams, and blackmail. [7] Tracing suspicious bitcoin entities Transaction features M Weber et al [8] Identifying illicit bitcoin users Transaction features Y Hu et al [9] Detecting Money Laundering Graph embeddings H Yin et al [10] Identifying illicit bitcoin users Transaction features L Nan et al [11] Mixing service detection Graph embeddings…”

Section: Description Of Bitcoin Systemmentioning

confidence: 99%

Detecting Illicit Entities in Bitcoin using Supervised Learning of Ensemble Decision Trees

Nerurkar

Busnel

Ludinard

et al. 2020

Proceedings of the 2020 10th International Conference on Information Communication and Management

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Since its inception in 2009, Bitcoin has been mired in controversies for providing a haven for illegal activities. Several types of illicit users hide behind the blanket of anonymity. Uncovering these entities is key for forensic investigations. Current methods utilize machine learning for identifying these illicit entities. However, the existing approaches only focus on a limited category of illicit users. The current paper proposes to address the issue by implementing an ensemble of decision trees for supervised learning. More parameters allow the ensemble model to learn discriminating features that can categorize multiple groups of illicit users from licit users. To evaluate the model, a dataset of 2059 real-life entities on Bitcoin was extracted from the Blockchain. Nine features were engineered to train the model for segregating 28 different licit-illicit categories of users. The proposed model provided a reliable tool for forensic study. Empirical evaluation of the proposed model vis-a-vis three existing benchmark models was performed to highlight its efficacy. Experiments showed that the specificity and sensitivity of the proposed model were comparable to other models.

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Bitcoin Mixing Detection Using Deep Autoencoder

Cited by 29 publications

References 6 publications

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Anomaly detection in blockchain using network representation and machine learning

Detecting Illicit Entities in Bitcoin using Supervised Learning of Ensemble Decision Trees

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