Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.
Albeit the primary usage of Bitcoin is to exchange currency, its blockchain and consensus mechanism can also be exploited to securely execute some forms of smart contracts. These are agreements among mutually distrusting parties, which can be automatically enforced without resorting to a trusted intermediary. Over the last few years a variety of smart contracts for Bitcoin have been proposed, both by the academic community and by that of developers. However, the heterogeneity in their treatment, the informal (often incomplete or imprecise) descriptions, and the use of poorly documented Bitcoin features, pose obstacles to the research. In this paper we present a comprehensive survey of smart contracts on Bitcoin, in a uniform framework. Our treatment is based on a new formal specification language for smart contracts, which also helps us to highlight some subtleties in existing informal descriptions, making a step towards automatic verification. We discuss some obstacles to the diffusion of smart contracts on Bitcoin, and we identify the most promising open research challenges.
Jan 1, 2018·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Mikkel Alexander Harlev, Haohua Sun Yin, Klaus Christian Langenheldt, Raghava Rao Mukkamala · 5 authors
Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity’s real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilised a sample of 434 entities (with ~ 200 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 10 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm, we achieve an accuracy of 77% and F1-score of ~ 0.75. We discuss our novel approach of Supervised Machine Learning for uncovering Bitcoin Blockchain anonymity and its potential applications to forensics and financial compliance and its societal implications, outline study limitations and propose future research directions.
We analyze the Bitcoin protocol for electronic peer-to-peer payments and the operations that support the "blockchain" that underpins it. It is shown that that protocol maps formally into a dynamic game that is an extension of standard models of R&D racing. The model provides a technical foundation for any economic analysis of 'proof of work' protocols. Using the model, we demonstrate that free entry is solely responsible for determining resource usage by the system for a given reward to mining. The endogenous level of computational difficulty built into the Bitcoin protocol does not mitigate this usage and serves only to determine the time taken to process transactions. Regulating market structure will mitigate resource use highlighting the importance of identifying the benefits of competition for the operation of the blockchain.
Blockchain-based cryptocurrencies have demonstrated how to securely implement traditionally centralized systems, such as currencies, in a decentralized fashion. However, there have been few measurement studies on the level of decentralization they achieve in practice. We present a measurement study on various decentralization metrics of two of the leading cryptocurrencies with the largest market capitalization and user base, Bitcoin and Ethereum. We investigate the extent of decentralization by measuring the network resources of nodes and the interconnection among them, the protocol requirements affecting the operation of nodes, and the robustness of the two systems against attacks. In particular, we adapted existing internet measurement techniques and used the Falcon Relay Network as a novel measurement tool to obtain our data. We discovered that neither Bitcoin nor Ethereum has strictly better properties than the other. We also provide concrete suggestions for improving both systems.
We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.
At their essence, blockchains are digital sequences of numbers coded into computer software that permit the secure exchange, recording, and broadcasting of transactions between individual users operating anywhere in the world with Internet access. Like most technological changes, the development of blockchains drew on and combined several existing technologies. Blockchains incorporate digital encryption technologies that mask, to varying degrees, the specific content exchanged as well as the identities of individual users. Algorithms, pre-coded series of step-by-step instructions, are also mobilised in solving complex mathematical equations and arriving at a consensus on the validity of transactions within networks of users. Time-stamping technologies then periodically bundle verified transactions into datasets, or ‘blocks’. Linked together sequentially, these ‘blocks’ form ‘chains’ that make up larger ‘blockchain’ databases of transactions that broadcast a permanent record of transactions whilst maintaining the anonymity of users and specific content exchanged. Blockchains are intended to be maintained by all users in manners meant to be immutable, unless users arrive at a clear consensus to undertake changes.
The concept of Bitcoin was first introduced by an unknown individual (or a group of people) named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is a peer-to-peer cryptocurrency and a decentralized worldwide payment system for digital currency where transactions take place among users without any intermediary. Bitcoin transactions are performed and verified by network nodes and then registered in a public ledger called blockchain, which is maintained by network entities running Bitcoin software. To date, this cryptocurrency is worth close to U.S. $150 billion and widely traded across the world. However, as Bitcoin's popularity grows, many security concerns are coming to the forefront. Overall, Bitcoin security inevitably depends upon the distributed protocols-based stimulant-compatible proof-of-work that is being run by network entities called miners, who are anticipated to primarily maintain the blockchain (ledger). As a result, many researchers are exploring new threats to the entire system, introducing new countermeasures, and therefore anticipating new security trends. In this survey paper, we conduct an intensive study that explores key security concerns. We first start by presenting a global overview of the Bitcoin protocol as well as its major components. Next, we detail the existing threats and weaknesses of the Bitcoin system and its main technologies including the blockchain protocol. Last, we discuss current existing security studies and solutions and summarize open research challenges and trends for future research in Bitcoin security.
In recent years, Tether issuances (or 'grants') have increased significantly, which correlated broadly with a significant rise in Bitcoin valuation. This paper examines the impact of cryptocurrency issuances on subsequent cryptocurrency returns. It is argued that as Tether is the undisputed 'stable coin', the minting of new Tether acts similarly to monetary expansion in cryptocurrency markets, inflating the prices of Bitcoin. We construct a VAR model and show contrary to investor expectations, Tether issuances do not impact subsequent Bitcoin returns, however, they do impact traded volumes. We also document an increase in Tether trading following a subsequent decrease in Bitcoin returns. This illustrates investor preferences for lower volatility crypto-assets in periods following negative Bitcoin returns.
Abstract In December 2017, both the Chicago Board Options Exchange and the Chicago Mercantile Exchange introduced futures contracts on bitcoin. We investigate to what extent they provide useful information for the price discovery of bitcoin. We rely on the information share methodology of Hasbrouck (1995, J Finance , 50, pp. 1175–1199) and Gonzalo and Granger (1995, J Bus Econ Stat, 13, pp. 27–35) and find that the spot price leads the futures price. We attribute this result to the higher trading volume and the longer trading hours of the globally distributed bitcoin spot market, compared to the relatively restricted access to the US‐based futures markets.
We address the valuation of bitcoins and other blockchain tokens in a new type of production economy: a decentralized financial network (DN). An identifying property of these assets is that contributors to the DN trust (miners) receive units of the same asset used by consumers of DN services. Therefore, the overall production (hashrate) and the bitcoin price are jointly determined. We characterize the demand for bitcoins and the supply of hashrate and show that the equilibrium price is obtained by solving a fixed-point problem and study its determinants. Price-hashrate “spirals” amplify demand and supply shocks.
ABSTRACT We offer a general equilibrium analysis of cryptocurrency pricing. The fundamental value of the cryptocurrency is its stream of net transactional benefits, which depend on its future prices. This implies that, in addition to fundamentals, equilibrium prices reflect sunspots. This in turn implies multiple equilibria and extrinsic volatility, that is, cryptocurrency prices fluctuate even when fundamentals are constant. To match our model to the data, we construct indices measuring the net transactional benefits of Bitcoin. In our calibration, part of the variations in Bitcoin returns reflects changes in net transactional benefits, but a larger share reflects extrinsic volatility.