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.
Cryptocurrencies are among the largest unregulated markets in the world. We find that approximately one-quarter of bitcoin users are involved in illegal activity. We estimate that around $76 billion of illegal activity per year involve bitcoin (46% of bitcoin transactions), which is close to the scale of the U.S. and European markets for illegal drugs. The illegal share of bitcoin activity declines with mainstream interest in bitcoin and with the emergence of more opaque cryptocurrencies. The techniques developed in this paper have applications in cryptocurrency surveillance. Our findings suggest that cryptocurrencies are transforming the black markets by enabling “black e-commerce.” Received June 1, 2017; editorial decision December 8, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Zcash is a fork of Bitcoin with optional anonymity features. While transparent transactions are fully linkable, shielded transactions use zero-knowledge proofs to obscure the parties and amounts of the transactions. First, we observe various metrics regarding the usage of shielded addresses. Moreover, we show that most coins sent to shielded addresses are later sent back to transparent addresses. We then search for round-trip transactions, where the same, or nearly the same number of coins are sent from a transparent address, to a shielded address, and back again to a transparent address. We argue that such behavior exhibits high linkability, especially when they occur nearby temporally. Using this heuristic our analysis matched 31.5% of all coins sent to shielded addresses.
Kentaroh Toyoda, Tomoaki Ohtsuki, P. Takis Mathiopoulos
Although Bitcoin is one of the most successful decentralized cryptocurrency, recent research has revealed that it can be used as fraudulent activities such as HYIP (High Yield Investment Program). To identify such undesired activities, it is important to obtain Bitcoin addresses related with fraud. So far, the identification of such activities is based upon relating Bitcoin addresses with graph mining procedures. In this paper, we follow a different approach for identifying Bitcoin addresses related with HYIP by analyzing transactions patterns. In particular, based on the individual inspection of HYIP activity in Bitcoin, we propose a number of features that can be extracted from transactions. In particular, a signed integer called pattern is assigned to each transaction and the frequency of each pattern is calculated as key features. By evaluating the classification performance with more than 1,500 labeled Bitcoin addresses, it is shown that about 83% of HYIP addresses are correctly classified while maintaining false positive rate less than 4.4%.
Bitcoin, a peer-to-peer payment system and digital currency, is often involved in illicit activities such as scamming, ransomware attacks, illegal goods trading, and thievery. At the time of writing, the Bitcoin ecosystem has not yet been mapped and as such there is no estimate of the share of illicit activities. This paper provides the first estimation of the portion of cyber-criminal entities in the Bitcoin ecosystem. Our dataset consists of 854 observations categorised into 12 classes (out of which 5 are cybercrime-related) and a total of 100,000 uncategorised observations. The dataset was obtained from the data provider who applied three types of clustering of Bitcoin transactions to categorise entities: co-spend, intelligence-based, and behaviour-based. Thirteen supervised learning classifiers were then tested, of which four prevailed with a cross-validation accuracy of 77.38%, 76.47%, 78.46%, 80.76% respectively. From the top four classifiers, Bagging and Gradient Boosting classifiers were selected based on their weighted average and per class precision on the cybercrime-related categories. Both models were used to classify 100,000 uncategorised entities, showing that the share of cybercrime-related is 29.81% according to Bagging, and 10.95% according to Gradient Boosting with number of entities as the metric. With regard to the number of addresses and current coins held by this type of entities, the results are: 5.79% and 10.02% according to Bagging; and 3.16% and 1.45% according to Gradient Boosting.
Blockchain technology can be utilized to improve gun control without changing existing laws. Firearm related mortality is at epidemic levels in the United States and not only has a significant impact upon public health, it also creates a large financial burden. Suicide is the most common way guns kill. Through better gun tracking and improved screening of high risk individuals, this technological advance in distributed ledger technology will improve background checks on individuals and tracing of guns used in crimes.
Purpose The purpose of this paper is to determine if Bitcoin transactions could be de-anonymised by analysing the Bitcoin blockchain and transactions conducted through the blockchain. In addition, graph analysis and the use of modern social media technology were examined to determine how they may help reveal the identity of Bitcoin users. A review of machine learning techniques and heuristics was carried out to learn how certain behaviours from the Bitcoin network could be augmented with social media technology and other data to identify illicit transactions. Design/methodology/approach A number of experiments were conducted and time was spend observing the network to ascertain how Bitcoin transactions work, how the Bitcoin protocol operates over the network and what Bitcoin artefacts can be examined from a digital forensics perspective. Packet sniffing software, Wireshark, was used to see whether the identity of a user is revealed when they set up a wallet via an online wallet service. In addition, a block parser was used to analyse the Bitcoin client synchronisation and reveal information on the behaviour of a Bitcoin node when it joins the network and synchronises to the latest blockchain. The final experiment involved setting up and witnessing a transaction using the Bitcoin Client API. These experiments and observations were then used to design a proof of concept and functional software architecture for searching, indexing and analyzing publicly available data flowing from the blockchain and other big data sources. Findings Using heuristics and graph analysis techniques show us that it is possible to build up a picture of behaviour of Bitcoin addresses and transactions, then utilise existing typologies of illicit behaviour to collect, process and exploit potential red flag indicators. Augmenting Bitcoin data, big data and social media may be used to reveal potentially illicit financial transaction going through the Bitcoin blockchain and machine learning applied to the data sets to rank and cluster suspicious transactions. Originality/value The development of a functional software architecture that, in theory, could be used to detect suspicious illicit transactions on the Bitcoin network.
In recent years, with the development of the Internet, network currency has gradually emerged. Bitcoin which is produced on the basis of complex algorithms has developed rapidly and attracted wide attention in academia. This paper explores the influence factors of bitcoin market transaction by analyzing the interaction between agents in bitcoin market transaction. Applying complex adaptive system modeling method based on multi-agent, this paper establishes an agent-based bitcoin market transaction model, and designs behavioral rules as well as transaction mechanism in detail for each agent in the process of market transaction. Then, we carry out a simulation on the Starlogo simulation platform and analyze the impact of the change in trader’s number on market transaction.
IntroductionIdentity verification and authentication has long been a critical component in service delivery for both the private and public sectors, but changing citizen demands in the digital age have stressed the need for new approaches to verify that an individual is who they say they are – with surety.
Decentralization, on one hand, brings more transparency and trust to the parties involved in transactions,but on the other hand, it narrows possibilities for central control. Distributed Ledger Technology(DLT) is a recent decentralized innovation in the field of information and communication technology(ICT) that acts as self-sustainable ledger for documenting transactions self-protected against counterfeitingand hacker attacks. The aim of the current research paper is to reveal opportunities and barriersfor utilization of distributed ledgers in the context of EU digital single market strategy. The main tasksare (1) to analyze functionality dynamics of existing distributed ledgers, (2) to analyze utilization areasof distributed ledgers, (3) to analyze digital trends related to utilization of distributed ledgers within theEU. The current research paper utilizes methods of content analysis, grounded theory, descriptive statistics,correlation analysis and regression analysis. The research has revealed that half of EU DigitalSingle Market priorities can be facilitated through distributed ledgers.DOI: http://dx.doi.org/10.5755/j01.eis.0.11.18134
While much has already been written about blockchain applications and prospects in the FinTech industry, little research has been done to explore blockchain technology’s user-centric paradigm in enabling various applications beyond banking. This article is an effort to contribute to that body of scholarship by exploring blockchain technology’s potential applications, and their limits, in areas that intersect with social impact, including human rights. This article explores whether blockchain technology and its core operational principles – such as decentralisation, transparency, equality and accountability – could play a role in limiting undue online surveillance, censorship and human rights abuses that are facilitated by the increasing reliance on a few entities that control access to information online. By doing so, this article aims at initiating a scholarly curiosity to understand what is possible and what is to be concerned about when it comes to the potential impact of blockchain technology on society.
Bitcoin is an emerging financial technology that is gaining a lot of popularity all over the world and starting to change the way people make financial transaction. In Indonesia however, bitcoin is more famous for its negative issue, such as money laundering, rather than its usage in financial transaction. One interesting fact to observe is the absence of regulations that explicitly governs the usage of bitcoins. This paper analyzes the factors influencing Indonesian authorities in defining regulations for bitcoins and how the existing regulations left open some opportunities for a bad guy to perform money laundering with the help of bitcoins. A qualitative approach is used in this research for data collection and analysis. Interview with two experts representing bitcoin exchange company and legal consultant was conducted in data collection. The outcome of this research is to find out vulnerabilities in bitcoin that allows money laundering and give suggestions on how to prevent money laundering with bitcoin from the perspective of regulations and bitcoin company in Indonesia.
When Bitcoin was first introduced to the world in 2008 by an enigmatic programmer going by the pseudonym Satoshi Nakamoto, it was billed as the world's first decentralized virtual currency. Offering the first credible incarnation of a digital currency, Bitcoin was based on the principal of peer to peer transactions involving a complex public address and a private key that only the owner of the coin would know. This paper will seek to investigate how the usage and value of Bitcoin is affected by current events in the cyber environment. Is an advancement in the digital security of Bitcoin reflected by the value of the currency and conversely does a major security breech have a negative effect? By analyzing statistical data of the market value of Bitcoin at specific points where the currency has fluctuated dramatically, it is believed that trends can be found. This paper proposes that based on the data analyzed, the current integrity of the Bitcoin security is trusted by general users and the value and usage of the currency is growing. All the major fluctuations of the currency can be linked to significant events within the digital security environment however these fluctuations are beginning to decrease in frequency and severity. Bitcoin is still a volatile currency but this paper concludes that this is a result of security flaws in Bitcoin services as opposed to the Bitcoin protocol itself.
Rebecca S. Portnoff, Danny Yuxing Huang, Periwinkle Doerfler, Sadia Afroz · 5 authors
Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.
The blockchain is a relatively new technology used to verify and store transaction records for online cryptocurrencies like Bitcoin. The system is redundant and distributed, making it difficult for transactions to be rescinded, duplicated, or faked. Beyond online currencies, the blockchain has potential uses in health care, education, and many other fields. This column will briefly describe what the blockchain is and how it is being used, potential future uses that may be of interest to librarians and medical practitioners, and some of the problems with the system.
This article describes how Blockchain is a technology that has a great potential to change the way business is done in the future, exactly like the internet did in the early nineties. Blockchain offers new opportunities to develop new types of digital services to overcome business problems, and improve business practices by making transaction information a public resource. While research on the topic is still emerging, it has mostly focused on crypto-currencies instead of taking advantage of this novel concept to create new advanced services. This article discusses blockchain and the technology behind it, some of its possible applications, as well as threats targeting the new poorly understood technology.
We investigate how distributed denial-of-service (DDoS) attacks and other disruptions affect the Bitcoin ecosystem. In particular, we investigate the impact of shocks on trading activity at the leading Mt. Gox exchange between April 2011 and November 2013. We find that following DDoS attacks on Mt. Gox, the number of large trades on the exchange fell sharply. In particular, the distribution of the daily trading volume becomes less skewed (fewer big trades) and had smaller kurtosis on days following DDoS attacks. The results are robust to alternative specifications, as well as to restricting the data to activity prior to March 2013, i.e., the period before the first large appreciation in the price of and attention paid to Bitcoin.
Purpose The purpose of this paper is to critically analyse research surrounding the anonymity of online transactions using Bitcoin and report on the feasibility of law enforcement bodies tracing illicit transactions back to a user’s real-life identity. Design/methodology/approach The design of this paper follows on from the approach taken by Reid and Harrigan (2013) in determining whether identifying information may be collated with external sources of data to identify individual users. In addition to conducting a detailed literature review surrounding the anonymity of users, and the potential ability to track transactions through the blockchain, four Bitcoin exchange services are examined to ascertain whether information provided at the sign-up stage is sufficiently verified and reliable. By doing so, this research tests the ability for law enforcement to reasonably rely upon this information when attempting to prosecute individuals. Additionally, by submitting fake information for verification, the plausibility of these services accepting fraudulent or illegitimate information is also tested. Findings It may be possible to identify and prosecute bad actors through the analysis of transaction histories by tracing them back to an interaction with a Bitcoin exchange. However, the compliance and implementation of anti-money laundering legislation and customer identification security standards are insufficiently used within some exchange services, resulting in more technologically adept, or well-funded, criminals being able to circumvent identification controls and continue to transact without revealing their identities. The introduction of and compliance with know-your customer and customer due diligence legislation is required before law enforcement bodies may be able to accurately rely on information provided to a Bitcoin exchange. This paper highlights the need for research to be undertaken to examine the ways in which criminals are circumventing identity controls and, consequently, financing their illicit activities. Originality/value By ascertaining the types of information submitted by users when exchanging real currency for virtual currency, and seeing whether this information may be accepted despite being fraudulent in nature, this paper elucidates the reliability of information that law enforcement bodies may be able to access when tracing transactions back to an individual actor.
Blockchain is the technology at the core of what could become the "Fintech" transformation of capital markets. It can potentially facilitate cheaper, more efficient and secure operations. The mechanism behind it is introduced in this paper, as are its uses and suggested areas for future academic research. The paper critically reviews the promise that blockchain and distributed ledgers will speed up financial settlements and transactions. In it we recommend financial institutions evaluate the adoption of blockchain and/or adapt their existing legacy systems to allow for digital clearing over the internet.
Malte Möser, Kyle Soska, Ethan Heilman, Kevin Lee · 11 authors
Abstract Monero is a privacy-centric cryptocurrency that allows users to obscure their transactions by including chaff coins, called “mixins,” along with the actual coins they spend. In this paper, we empirically evaluate two weaknesses in Monero’s mixin sampling strategy. First, about 62% of transaction inputs with one or more mixins are vulnerable to “chain-reaction” analysis - that is, the real input can be deduced by elimination. Second, Monero mixins are sampled in such a way that they can be easily distinguished from the real coins by their age distribution; in short, the real input is usually the “newest” input. We estimate that this heuristic can be used to guess the real input with 80% accuracy over all transactions with 1 or more mixins. Next, we turn to the Monero ecosystem and study the importance of mining pools and the former anonymous marketplace AlphaBay on the transaction volume. We find that after removing mining pool activity, there remains a large amount of potentially privacy-sensitive transactions that are affected by these weaknesses. We propose and evaluate two countermeasures that can improve the privacy of future transactions.
Bitcoin is the most famous cryptocurrency currently operating with a total marketcap of almost 7 billion USD. This innovation stands strong on the feature of pseudo anonymity and strives on its innovative de-centralized architecture based on the Blockchain. The Blockchain is a distributed ledger that keeps a public record of all the transactions processed on the bitcoin protocol network in full transparency without revealing the identity of the sender and the receiver. Over the course of 2016, cryptocurrencies have shown some instances of abuse by criminals in their activities due to its interesting nature. Darknet marketplaces are increasing the volume of their businesses in illicit and illegal trades but also cryptocurrencies have been used in cases of extortion, ransom and as part of sophisticated malware modus operandi. We tackle these challenges by developing an analytical capability that allows us to map relationships on the blockchain and filter crime instances in order to investigate the abuse in law enforcement local environment. We propose a practical bitcoin analytical process and an analyzing system that stands alone and manages all data on the blockchain in real-time with tracing and visualizing techniques rendering transactions decipherable and useful for law enforcement investigation and training. Our system adopts combination of analyzing methods that provides statistics of address, graphical transaction relation, discovery of paths and clustering of already known addresses. We evaluated our system in the three criminal cases includes marketplace, ransomware and DDoS extortion. These are practical training in law enforcement, then we determined whether our system could help investigation process and training.