A first estimation of the proportion of cybercriminal entities in the bitcoin ecosystem using supervised machine learning

Yin, Haohua Sun; Vatrapu, Ravi

doi:10.1109/bigdata.2017.8258365

Cited by 62 publications

(17 citation statements)

References 9 publications

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“…In recent years, different machine learning algorithms and techniques had also been taken into account to generate abnormal profits by exploiting the inefficiency of the cryptocurrency market [12]. The involvement of some cryptocurrencies including Bitcoin in illicit activities can also be accurately measured using machine leaning approach [13].…”

Section: Introductionmentioning

confidence: 99%

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Chowdhury

Rahman

et al. 2020

Physica A: Statistical Mechanics and its Applications

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At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.

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Section: Introductionmentioning

confidence: 99%

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Chowdhury

Rahman

et al. 2020

Physica A: Statistical Mechanics and its Applications

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“…In 2017, Yin and Vatrapu [65] built several different classifiers using supervised machine learning models, to identify the Bitcoin addresses that are related to criminal activities. The next year, Harlev et al [66] also introduced a supervised learning model with the gradient boosting algorithm.…”

Section: Transaction Entity Classificationmentioning

confidence: 99%

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Du¹,

Miao²,

Tong³

et al. 2022

Blockchain Potential in AI

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Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence.

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“…A number of supervised ML based approaches were used in the context of cryptocurrency blockchain. A previous study in [16] estimated the proposition of cybercriminal entities in the Bitcoin using supervised ML. They tested thirteen supervised ML classifiers, and prioritized them according to the crossvalidation accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Peer Connectivity Estimation for Reliable Blockchain Networking

Kim

Nakashima

Fan

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Peer connectivity plays a significant role in a blockchain network since any poor connectivity may result in the nodes operating on outdated data (e.g., cryptocurrency transactions). Although connectivity information is maintained by individual nodes, such identifier-based information might be unreliable due to the possibility of bogus identifiers. This paper tackles the problem of peer connectivity estimation through datadriven analytics of blockchain traffic for reliable blockchain networking. We define a set of variables to represent traffic characteristics and estimate peer connectivity from the collected data using a machine learning methodology. We also investigate the feasibility of feature prioritization to minimize estimation complexities. Our experimental results show that the presented estimation mechanism makes accurate predictions, with less than 0.1 difference between the measurement and estimation for over 99.7% of predictions. The time complexity measured on a commodity machine shows a microsecond scale for completing a single prediction task, enabling real-time operations.

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A first estimation of the proportion of cybercriminal entities in the bitcoin ecosystem using supervised machine learning

Cited by 62 publications

References 9 publications

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

A Machine Learning Approach to Peer Connectivity Estimation for Reliable Blockchain Networking

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