Blockchain Papers

Chaehyeon Lee, Sajan Maharjan, Kyungchan Ko, James Won‐Ki Hong

No abstract is available for this record.

Computer Science Engineering, NIET, Greater Noida, India., Smiley Gandhi, Mohammad Shabaz

Evidence and witness play an important role to investigate crime and lawful jurisdiction in any case. But often, the victims do not get justice due to the middlemen and the altered evidences in the centralized network. Block chain is an ideal solution to ensure transparency till the highest hierarchy of jurisdiction using a decentralized peer to peer network to store data which is immutable. It is like a distributed ledger working on the proof of work algorithm that validates each amendment and modification made in a particular chain. Evichain inculcates the transparency and security of the same decentralized network in the crime investigation process. It is an application on which the entire data regarding any particular investigation is stored in a block chain with limited people having access rights. It also includes cases filed by the victims themselves. Every amendment made in the block chain is validated. Also the data access is restricted to the users with that specific private key.

Nikita Singh, Tarun Kumar, Manu Vardhan

This research work proposes a novel and comprehensive electronic cheque transactions framework. The proposed e-cheque system is free from the various security attacks such as alteration of the e-cheque, double spending of e-cheque, counterfeits e-cheques. The e-cheque generated in the proposed system can be deposited electronically or physically via teller machines. This facility provides greater flexibility to the customers of the banking system. The proposed system also provides space for professional miners to participate in the e-cheque transaction system by performing mining's and earn incentives. As the customer's perspective of security, the proposed system uses digital signature and cryptographic hash in each transaction hence it is a completely secure system. The existing CTS based cheque clearance request requires at least one day to clear a cheque which could extend to two or three days but the proposed system requires only 1.65 seconds for clearing any e-cheque.

Harsh Jot Singh, Abdelhakim Hafid

No abstract is available for this record.

Yujing Lin, Po-Wei Wu, Cheng-Han Hsu, I‐Ping Tu · 5 authors

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-Fl /Macro-F1 of 87% /86% with LightGBM.

Shivansh Pandey, Shivam Goel, Subodh Bansla, Dhiraj Pandey

Online crowdfunding enables people to raise funds for their project. People who are interested in a project can donate by making an online transaction. The donated money goes to the project manager, which he uses to complete the project or to make a product. This existing method of online crowdfunding has a major drawback. It does not allow contributors to have control over the money they have contributed. Since in the existing method the project manager has all the control over the money contributed he can very easily perform malicious activities. Here we address this problem faced by the existing online crowdfunding platforms by using ethereum network and smart contract. The development of Blockchain technology has allowed businesses to build decentralized models. It has derived new methods to conduct transactions and make agreements. One of the technologies that propose an alternative to the traditional model is the smart contract. A smart contract is similar to a contract in the physical world, but it is digital and represented by a tiny computer program stored in a blockchain. These smart contracts can be used to implement logic. A method has been proposed here that uses smart contract to manage all the activities performed in a crowdfunding campaign. The proposed method has been implemented and its various features are tested by funding campaigns on rinkeby test network.

Seongho Hong, Heeyoul Kim

This bitcoin system is a system made to support transactions between users without the help of any financial institution. The Bitcoin Exchange is a service that enables exchanges of bitcoins mined by others with cash. To use this service, the miner should send bitcoins from his bitcoin wallet to the deposit address provided by the Exchange. The bitcoins delivered to the deposit address are automatically moved to one of the Exchange's bitcoin addresses by the Exchange system. Since what is known to the user is only the deposit address for the user, the user can only guess that the Exchange has taken the bitcoins from his deposit address when he/she sees the transfer of his/her bitcoins to another address. We will present a method to find out a few bitcoin addresses through preliminary work and find out the associated Exchange addresses using the characteristics of the deposit addresses and system addresses of the Exchange through transactions with the relevant addresses on the blockchain.

Weili Chen, Zibin Zheng, Edith C.‐H. Ngai, Peilin Zheng · 5 authors

Blockchain technology becomes increasingly popular. It also attracts scams, for example, a Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help to deal with this issue and to provide reusable research data sets for future research, this paper collects real-world samples and proposes an approach to detect Ponzi schemes implemented as smart contracts (i.e., smart Ponzi schemes) on the blockchain. First, 200 smart Ponzi schemes are obtained by manually checking more than 3,000 open source smart contracts on the Ethereum platform. Then, two kinds of features are extracted from the transaction history and operation codes of the smart contracts. Finally, a classification model is presented to detect smart Ponzi schemes. The extensive experiments show that the proposed model performs better than many traditional classification models and can achieve high accuracy for practical use. By using the proposed approach, we estimate that there are more than 500 smart 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.

Wonseok Choi, Hyoungshick Kim

Since Bitcoin appeared in 2009, various other cryptocurrencies have also begun to attract attention and supporters. At the same time, because many cryptocurrency investors want to make a profit using cryptocurrencies, cryptocurrency exchanges list them without any special or technical verification. Cryptocurrency developers often write white papers to describe their cryptocurrency's special techniques and expected future worth. However, based solely on the contents of the white paper and/or a surface inspection of the source code, we cannot know if the developer has simply leveraged existing code bases without incorporating novel functionality. In order to address this problem, we present a framework to measure the similarity between the source codes of cryptocurrencies for detecting plagiarism.

Mark D. Sheldon

SUMMARY A perennial challenge in the accounting profession is how to aggregate and share instances of practitioner misconduct among numerous relevant parties. At present, both the American Institute of Certified Public Accountants (AICPA) and National Association of State Boards of Accountancy (NASBA) offer solutions for centralized collection of misconduct, but both likely experience issues with incomplete reporting from key constituents. I propose a novel use of blockchain technology to address this issue, such that all key parties in the accounting profession leverage an accountancy blockchain to aggregate and share instances of practitioner misconduct across the country on a nearly real-time basis. Such a network creates an immutable record of misconduct and allows key constituents in the accounting profession to work together and share information as peers without the risk of one party taking control of the ledger. I close by discussing blockchain-specific roadblocks to realizing this proposed model.

Pedro W. Abreu, Manuela Aparício, Carlos J. Costa

Blockchain technology is already being talked about as one of the megatrends for the next years. Researchers and organisations are starting to understand the potential benefits of this technology and are exploring how it can disrupt the world we live in with a diverse range of applications. But the truth is the ability to move blockchain from concept to adoption and production has been minimal yet. When it comes to auditing, blockchain solutions could have important benefits by reducing the workload of the auditors, helping in minimising fraud and optimising the existing processes but is also vital to have in mind other emerging technologies. Factom, Libra, and Verady are some examples of companies developing blockchain solutions that can be applied in the auditing environment, but much of the necessary development is still yet to be done.

Paris Moore

Both “big data” and “analytics” have become popular keywords in many organizations. The power data analytics has on harnessing the increasing volumes, velocity and complexity of data in a world of constant change and disruptive technologies has been recognized. Many companies are making significant investments to better understand the impact of these capabilities on their businesses. One area with significant potential is the transformation of the audit. This project explores ways in which analytics can change and shape the work of accountants.
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\nAnomaly detection plays a pivotal role in data mining since most outlying points contain crucial information for further investigation. In the financial world which the Bitcoin network is a part of, anomaly detection can indicate fraud. Using data mining tools such as Regression, we simultaneously examine the relationship among variables whilst visually inspecting the data for possible outliers. By doing so, I have chosen the world’s leading cryptocurrency, Bitcoin. This project will conclude with an in-depth analysis on whether or not data analytics can shape how effectively, and secure accountants can audit transactions by implementing analytics tools into their daily protocols.

Beltrán Borja Fiz Pontiveros, Robert Norvill, Radu State

Mining pools are collection of workers that work together as a group in order to collaborate in the proof of work and reduce the variance of their rewards when mining. In order to achieve this, Mining pools distribute amongst the workers the task of finding a block so that each worker works on a different subset of the candidate solutions. In most mining pools the selection of transactions to be part of the next block is performed by the pool manager and thus becomes more centralized. A mining Pool is expected to give priority to the most lucrative transactions in order to increase the block reward however changes to the transaction policy done without notification of workers would be difficult to detect. In this paper we treat the transaction selection policy performed by miners as a classification problem; for each block we create a dataset, separate them by mining pool and apply feature selection techniques to extract a vector of importance for each feature. We then track variations in feature importance as new blocks arrive and show using a generated scenario how a change in policy by a mining pool could be detected.

Yunsen Wang

This dissertation consists of three essays that design and evaluate the continuous audit analytics and fraud prevention systems using three emerging technologies (i.e., the blockchain, in-memory cloud computing, and deep learning). The first essay designs a framework of Blockchain-based Transaction Processing System using the homomorphic encryption and zero-knowledge proof mechanisms. Furthermore, this study develops a prototype of the designed system to demonstrate its applications in real-time accounting, continuous monitoring, and fraud prevention. Although the simulation tests show the Blockchain-based Transaction Processing System consumes more computational overhead than the conventional database-based ERP system, the blockchain should be considered as a promising technology for future accounting and auditing practice. The second essay introduces the database architecture that manages data in main physical memory and columnar format. This essay proposes a conceptual framework for applying the in-memory columnar database system to support high-speed continuous audit analytics. Moreover, this study develops a prototype and conducts the simulation tests to evaluate the proposed framework. The test results show the high efficiency and effectiveness of the in-memory columnar database relative to the conventional ERP system regarding the computational time and the storage volume. Furthermore, the deployment of the in-memory columnar database to the cloud shows great promise of applying the in-memory columnar database for continuous audit analytics. The third essay designs a continuous fraud detection system based on modified deep learning technology. Specifically, this essay builds an accounting layer on top of the deep learning architecture to process financial data for predicting the fraudulent financial statements. A prototype is developed to evaluate the prediction accuracy of the proposed design. The test results show the deep learning-based continuous fraud detection system provides high prediction accuracy relative to the existing studies of financial statement fraud detection.

Yanfeng Wang, Feng Li, Jinya Hu, Dong Zhuang

This paper addresses recognizing fraud users on a Bitcoin exchange website-bitcoin-otc. According to online rating records provided by the website, some users behave significantly different from others. Seeing that, the classical K-means clustering algorithm is proposed to identify these abnormal users. K-means algorithm is an unsupervised clustering algorithm that clusters users based on feature similarity. Therefore, performance of K-means algorithm relies on the features. This paper explored and found the best collection of features based on real record data, e.g., mean of total ratings sent. Since the selected features are not observed for record set, the website should offer these features for potential traders.

Nashirah Abu Bakar

Bitcoin is the first decentralized peer-to-peer payment network that is powered by its users with no central authority or middlemen. The objective of this study is to evaluate the normality of data distribution for exchange rate of Bitcoin. The method implemented in this study is Shapiro-Wilk normality test including graphical approach namely box plot .Results show the data distribution of exchange rate for Bitcoin follows non-normal distribution. Therefore, the normality transformation is important to make sure the distribution of data follows normal distribution. The normal distribution is very crucial as one of the requirement for validity of statistical test.Normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed.This study implemented two-stages of outliers detection and deletion process.The final results shows the distribution of Bitcoin exchange rate with first difference is follow normal distribution with probability of 0.722.Result concluded the distribution of data after second stages of outlies deletion treatment shows high normal distribution characteristics. This finding concludes that Bitcoin data is highly volatile with existence of many outliers. The transformation process is highly important to make sure the Bitcoin data follows normal distribution that underlying critical assumption for statistical tests.

Patrick M. Monamo, Vukosi Marivate, Bhekisipho Twala

In the Bitcoin network, lack of class labels tend to cause obscurities in anomalous financial behaviour interpretation. To understand fraud in the latest development of the financial sector, a multifaceted approach is proposed. In this paper, Bitcoin fraud is described from both global and local perspectives using trimmed k-means and kd-trees. The two spheres are investigated further through random forests, maximum likelihood-based and boosted binary regression models. Although both angles show good performance, global outlier perspective outperforms the local viewpoint with exception of random forest that exhibits nearby perfect results from both dimensions. This signifies that features extracted for this study describe the network fairly.

Thai Pham, Steven Lee

The problem of anomaly detection has been studied for a long time. In short, anomalies are abnormal or unlikely things. In financial networks, thieves and illegal activities are often anomalous in nature. Members of a network want to detect anomalies as soon as possible to prevent them from harming the network's community and integrity. Many Machine Learning techniques have been proposed to deal with this problem; some results appear to be quite promising but there is no obvious superior method. In this paper, we consider anomaly detection particular to the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use three unsupervised learning methods including k-means clustering, Mahalanobis distance, and Unsupervised Support Vector Machine (SVM) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes.

Patrick M. Monamo, Vukosi Marivate, Bhekisipho Twala

The rampant absorption of Bitcoin as a cryptographic currency, along with rising cybercrime activities, warrants utilization of anomaly detection to identify potential fraud. Anomaly detection plays a pivotal role in data mining since most outlying points contain crucial information for further investigation. In the financial world which the Bitcoin network is part of by default, anomaly detection amounts to fraud detection. This paper investigates the use of trimmed k-means, that is capable of simultaneous clustering of objects and fraud detection in a multivariate setup, to detect fraudulent activity in Bitcoin transactions. The proposed approach detects more fraudulent transactions than similar studies or reports on the same dataset.