Identification of High Yielding Investment Programs in Bitcoin via Transactions Pattern Analysis

Toyoda, Kentaroh; Ohtsuki, Tomoaki; Mathiopoulos, P. Takis

doi:10.1109/glocom.2017.8254420

Cited by 53 publications

(46 citation statements)

References 12 publications

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“…Capitalizing on this need, we have recently proposed a HYIP operators' Bitcoin addresses identification methodology [7]. The main idea behind this work is that when a Bitcoin address is tested, its transactions characteristics, which are also known as features, are calculated from its transaction history, and then designing a machine learning classifier by training the characteristics of HYIP and non-HYIP.…”

Section: Introductionmentioning

confidence: 99%

“…The main idea behind this work is that when a Bitcoin address is tested, its transactions characteristics, which are also known as features, are calculated from its transaction history, and then designing a machine learning classifier by training the characteristics of HYIP and non-HYIP. As the work reported in [7] is rather preliminary, there are a number of important issues which need to be further investigated. The first one is that the dataset used for evaluation in [7] is rather limited, as only 43 HYIPs and 1,523 non-HYIPs have been considered.…”

Section: Introductionmentioning

confidence: 99%

“…As the work reported in [7] is rather preliminary, there are a number of important issues which need to be further investigated. The first one is that the dataset used for evaluation in [7] is rather limited, as only 43 HYIPs and 1,523 non-HYIPs have been considered. Hence, an efficient HYIP operators' address collection method is required in order to obtain reliable results.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Methodology for HYIP Operators’ Bitcoin Addresses Identification

2019

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Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate. INDEX TERMS Bitcoin, blockchain analysis, forensics, data mining, HYIP (high yield investment programs).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Methodology for HYIP Operators’ Bitcoin Addresses Identification

2019

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“…Besides graph patterns, transaction history can also provide information for address identification. In [22], a set of features are proposed to summarize the transaction history and to identify addresses associated with HYIP based on supervised learning algorithms. These features are extended to identify seven types of Bitcoin-enabled services [5].…”

Section: Related Workmentioning

confidence: 99%

An Evaluation of Bitcoin Address Classification based on Transaction History Summarization

Lin

Hsu

et al. 2019

2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

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Bitcoin is a cryptocurrency that features a distributed, decentralized and trustworthy mechanism, which has made Bitcoin a popular global transaction platform. The transaction efficiency among nations and the privacy benefiting from address anonymity of the Bitcoin network have attracted many activities such as payments, investments, gambling, and even money laundering in the past decade. Unfortunately, some criminal behaviors which took advantage of this platform were not identified. This has discouraged many governments to support cryptocurrency. Thus, the capability to identify criminal addresses becomes an important issue in the cryptocurrency network. In this paper, we propose new features in addition to those commonly used in the literature to build a classification model for detecting abnormality of Bitcoin network addresses. These features include various high orders of moments of transaction time (represented by block height) which summarizes the transaction history in an efficient way. The extracted features are trained by supervised machine learning methods on a labeling category data set. The experimental evaluation shows that these features have improved the performance of Bitcoin address classification significantly. We evaluate the results under eight classifiers and achieve the highest Micro-F1 / Macro-F1 of 87% / 86% with LightGBM.

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“…On a different direction inside this area, [19] tried to identify financial frauds known as Ponzi schemes in Bitcoin. The authors created a set of features extracted from transactions and fed them into Random Forest and XGBoost to obtain a classification result that indicates potential fraud activity.…”

Section: Related Workmentioning

confidence: 99%

Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data

Brinckman

Kuehlkamp

Nabrzyski

et al. 2019

2019 International Conference on Information and Communication Technology Convergence (ICTC)

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As the public Ethereum network surpasses half a billion transactions and enterprise Blockchain systems becoming highly capable of meeting the demands of global deployments, production Blockchain applications are fast becoming commonplace across a diverse range of business and scientific verticals. In this paper, we reflect on work we have been conducting recently surrounding the ingestion, retrieval and analysis of Blockchain data. We describe the scaling and semantic challenges when extracting Blockchain data in a way that preserves the original metadata of each transaction by cross referencing the Smart Contract interface with the on-chain data. We then discuss a scientific use case in the area of Scientific workflows by describing how we can harvest data from tasks and dependencies in a generic way. We then discuss how crawled public blockchain data can be analyzed using two unsupervised machine learning algorithms, which are designed to identify outlier accounts or smart contracts in the system. We compare and contrast the two machine learning methods and cross correlate with public Websites to illustrate the effectiveness such approaches.

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Identification of High Yielding Investment Programs in Bitcoin via Transactions Pattern Analysis

Cited by 53 publications

References 12 publications

A Novel Methodology for HYIP Operators’ Bitcoin Addresses Identification

A Novel Methodology for HYIP Operators’ Bitcoin Addresses Identification

An Evaluation of Bitcoin Address Classification based on Transaction History Summarization

Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data

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