Blockchain Papers

Preeti Sharma, R. M. Pramila

No abstract is available for this record.

Sandesh Walunj, Akshay Gupta, Anuradha Sonone, Saurabh Kumar Yadav · 5 authors

Currently, the composition and structure of the production industry's supply chain is becoming increasingly complex. The loss and untimely transmission of supply chain information exacerbated the bullwhip effect. At the same time, due to the lack of a reliable repository of information, difficulties in traceability and accountability have also made supply chain management difficult. Blockchain has the characteristics of supporting distributed networks, synchronization of information between nodes, digital encryption, traceable information and unforgeable block content, which is suitable for use in supply chain and can provide a solution for it. In this paper, a design scheme of an integrated platform for information services provided by supply chain participants and based on the Ethereum blockchain is proposed. Using Ethereum smart contracts, the regular trade involved in the supply chain is realized using blockchain technology, and key information about the production and circulation of the supply chain is stored on the blockchain to ensure that the information cannot be falsified. At the same time, a reputation evaluation method based on smart contracts is used to evaluate the reputation of enterprises in the supply chain, which can provide references for supplier selection among enterprises.

Njoku ThankGod Anthony, Mahmoud Shafik, Fatih Kurugöllü, Hany F. Atlam

Over the past few years, Blockchain technology has been utilized in various applications to improve privacy and security. Although blockchain has proven its worth as a very powerful technology, research has shown that it is not entirely immune to security and privacy attacks. There was a successful 51% attack on Ethereum Classic back in January 2019 which shows that blockchain still facing security and privacy challenges. This paper aims to develop an anomaly detection solution for the Ethereum blockchain to overcome security challenges using Machine Learning (ML). The proposed solution focuses on using a dynamic approach where the normal operational behaviour of the Ethereum blockchain is used to train ML algorithms and any deviation will be tagged as an anomaly and will be detected by the system. Four ML algorithms including K-Nearest Neighbours (KNN), Gaussian Naive Bayes (GaussianNB), Random Forest, and Stochastic Gradient Descent (SDG) were utilized to train and verify the accuracy of the proposed solution. The experimental results demonstrated that the random forest algorithm provided the best accuracy of 99.84% over other ML algorithms.

Jingjing Li, Xinge Rao, Xianyi Li, Sihai Guan

In recent years, the bitcoin market has developed rapidly and has been recognized as a new type of gold by many investors. It may replace gold as a hedge against inflation and become a new investment asset for financial management. The investment relationship with gold has increasingly important research value and practical significance. This paper modeled daily price flow data from 11 September 2016 to 10 September 2021 to help market traders determine whether they need to buy, hold, or sell assets in their portfolios daily. The model predicts price fluctuations through linear regression prediction of machine learning, K-Nearest Neighbor (KNN) algorithm. In the linear regression prediction, the goodness of fit of gold is 89.44%, and the goodness of fit of Bitcoin is 98.43%. In the test set prediction of KNN algorithm, the goodness of fit of gold is 97.25%, and the goodness of fit of Bitcoin is 95.06%. Based on this, the optimal investment strategy and the initial investment value are obtained. Empirical analysis shows that bitcoin price volatility and gold price volatility have a strong substitution effect; gold and currency used will be a suitable combination of hedging, which will bring momentum for the development of the market economy and become an important force in the sustainable development of a high-quality-driven economy.

Sihan Niu

As a focus in the field of financial innovation, blockchain technology is essentially a distributed ledger database, and it is open and transparent, decentralized and immutable in practical application. In the steady development of financial technology, blockchain technology, as a basic content, has attracted the attention of researchers and scholars, and from this, the payment system of virtual currency has been constructed. This greatly facilitates people’s daily life. Therefore, based on the understanding of the definition of blockchain technology and the influence of the content of this technology on the payment system, this paper deeply discusses the convenient payment system and its operation mode with blockchain technology as the core, and finally conducts verification analysis under the new cross-border payment mode. The results show that the virtual currency payment system based on blockchain technology is feasible and effective in practice. Especially in the social economy with the acceleration of network technological innovation, increasingly frequent international exchanges and constantly improved trade modes, the application of block chain technology contribute to the economic development of our country and gradually integrate into the international market, but from the point of view of the whole system security and technical performance, there are still many problems.

K. Vanitha, Subhashish Goswami, TNVRSwamy, K. Chitra Chellam · 6 authors

Blockchain Technology has acquired notoriety in both scholar and industry due to its decentralized, versatile, security and irrefutable characteristics. The blockchain is a disseminated data set that holds record of exchanges that are conveyed among members in its most essential structure. Manufactured exchanges can't pass aggregate certification since every exchange requires the understanding of various individuals. A record can never be changed or erased after it has been made and recognized by the blockchain. After the Internet, blockchain innovation is presently viewed as the main development. The previous could tackle the trust issue utilizing shared systems administration and public-key cryptography assuming the last option interfaces individuals to all the more likely comprehend online business processes. The objective of this paper is to take a gander at the critical instances of blockchain innovation's comprehensive effect and see it as an inseparable part of our daily existences.

Musan Eltuhami, Munaisyah Abdullah, Bazilah A. Talip

Currently, identity and document verification use either physical documentation or centralized digital databases. A central figure is responsible for verifying these documents and this is thought to prevent document tampering. However, this does not eliminate threats of external hacking or internal bad actors and individuals to commit acts of forgery. in addition, bureaucracy and delays often occur with central entities validating documents that causes wasted time for users. Blockchain technology has brought a positive impact to document traceability and transparency across institutions and industries. This research will analyse and use non-transferable non-fungible tokens (NFTs) with smart contract functions which is a new concept for projecting digital and physical goods on a blockchain, it’s expected to represent complicated operations in an effective manner for document traceability systems, enabling a decentralized approach while preventing fraud, corruption, tampering, and counterfeiting.

Trần Kim Toại, Thanh Thi Tuyet Le, Thinh Tien Bui, Vắng Quang Đàng · 5 authors

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies.

Mehmet Ali DEMİR, Ramazan Aktaş

In recent years, as a result of the increasing popularity of crypto-assets all over the world, an increasing number of innovative crypto assets are coming to the market. In this context, Fan Tokens, which is a type of "utility token", are also discussed in this research as a prominent crypto asset in recent years. As developments in both cryptocurrency and sports sector increase the interest in Fan Tokens day by day, the financial volume of the system is constantly growing. With the growing volume, the risks taken by those who buy Fan Tokens are also increasing. Although there are studies on the risks of crypto assets in the literature, since the issue is not addressed specifically for Fan Tokens, it is deemed worthy of our review. The relationship between the price movements of Fan Tokens and the movements of the dominant crypto assets has been examined. It has been determined by the regression and correlation analyzes in the research that the movements in the crypto money exchanges have an effect on the Fan Token exchanges. Therefore, the developments in the crypto money exchanges should be followed especially and carefully for those who see the Fan Tokens as an investment tool.

Ersin Şener, İbrahim Demir

Cryptocurrencies, which occupy a risky position among investment instruments, continue their technological developments day by day with the speed of money transfers and the confidence in the decentralization of production. In this paper, we propose a denoised Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) method for predicting cryptocurrency prices. The daily cryptocurrency price data of Bitcoin (BTC) is collected from a freely available website (cryptocompare.com). For the prediction of BTC/USD price, we considered the average values of daily opening, high, low and closing price as OHLC value. The data set is denoised from white noise using the discrete wavelet transform method (DWT) by VisuShrink thresholding on Daubechies (db4) wavelet at level=5. OHLC and denoised OHLC (DOHLC) values are predicted using ARIMA(p,d,q) and LSTM methods. LSTM hyperparameters are evaluated using 28 different combinations. A pair of Adam-linear optimization and activation function is the best hyperparameter with the lowest mean loss value of 1.42e-03. Finally, DLSTM was found to be the best prediction method according to the Root Mean Squared Error (RMSE) prediction metric: 556.85.

Sahar Erfanian, Yewang Zhou, Amar Razzaq, Azhar Abbas · 6 authors

Bitcoin (BTC)-the first cryptocurrency-is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research's innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.

M. J. Jeyasheela Rakkini, K. Geetha

Blockchain, a disruptive technology, has many applications in the domain of Finance, banking, real estate, insurance, supply chain, gaming industry with much more plethora of applications in near future. In spite of the decentralized, distributed, transparent, tamper-proof, data- provenance nature of the blockchain, it is subject to a lot of security attacks such as forking attacks and block withholding attacks. One such attack under the forking attack is selfish mining, which targets the reward distribution and also the difficulty adjustment algorithms(DAA). An exhaustive surve is done on the existing approaches to detect selfish mining and also on the profitability of selfish mining attacks. This survey is organized particularly around the aspects of selection or exploration of the shortest branch of the blockchain when a fork occurs. We aim to identify the implications of selecting the shorter branch of the fork in the blockchain, especially after 2016 blocks, where a difficulty adjustment occurs. Our survey focuses on the deployment of machine learning and deep learning, reinforcement methods on mitigating the selfish mining attacks in the blockchain.

Xiao Gao, Wenyin Zhang, Bin Zhao, Jiqun Zhang · 6 authors

To tackle the problems of questioning the authenticity of products faced in e-commerce live streaming and the low data integrity in the process of product transactions, the concept of traceability and a product authentication scheme that integrates blockchain technology and traceability are proposed. The proposed scheme deploys the product authentication strategy on the Ethereum blockchain in the form of a smart contract and calls the product authentication method on the traceability body by executing the smart contract to realize the authentication of product quality. A combination of on-chain and off-chain is used to store private information to guarantee information integrity. Security analysis results show that the proposed scheme can make the risk of information leakage lower and data security higher under the premise of achieving product certification. A system simulation experiment shows that the solution can meet product certification requirements and have a favorable impact on the live commerce industry.

Naman Shah, Sonal R Dave

Among the new way of exchanging money, using crypto currency has been very popular. Its also an investment to get good returns over the period of time. Cryptocurrency has grown to more than 120 million investors around the world as per a survey of 2021.Its growing at the 15 to 20% ratio around the world every year. This fact leads to a serious consideration of security and its vulnerabilities in block chain. Apart from market risks, high volatility, lack of rules and regulations, cyber risks are one of the most required types which needs proper attention and technical understanding. Because the crypto currencies are fully decentralized the risk of attacks is exposed and in most of the cases defenseless. Proof of stake and proof of work are two major algorithms followed by almost all crypto currencies to allot stocks to the holders. In this paper, different types of risks and attacks with POS and POW are explained with its mitigation. The problems and outcomes are examined, reviewed and conferred in case of Ethereum and Bitcoin crypto currencies. These currencies decentralized frameworks and anonymity attracts unlawful activities. Recognizing and preventing them needs understanding of the mechanism of attacks which are discussed in easiest possible ways for even a new-bee or an outsider person.

Oleksandr Byzkrovnyi, Kirill Smelyakov, Anastasiya Chupryna

This paper describes approaches to forecast Ethereum price based on regression analysis which are based on defined in this research list of factors which may affect price. These parameters can be a part of fundamental and technical analysis. In scope of forecasting the nonlinear regression models are used and compared, in couple with prediction of each factor which is used for regression by NeuralProphet. The models’ outputs were retrieved during experiment. Also, experiment includes models tuning to have more accurate result. The data time window for experiment is one year. This paper does not consider influence of political situation and nature cataclysms on cryptocurrency. Also, this research does not include index of openness of countries finance institute. The type of analyzed crypto is decentralized finance. The Java microbenchmark harness is used to calculate time which is spent for models training. Models’ performance is calculated by evaluation of regression metrics: Root mean square error, Mean absolute error, Mean square error, Explained variance.

K. Chaitanya Kumar, M. Rajesh

In the Present years the requirement for performing secure exchanges online have been designated by the utilization of Virtual tokens called Digital forms of money. Financial backers can bring in cash by performing mining exercises of Digital forms of money like Ethereum and Binance coin or basically selling Ethereum and Binance coin at a benefit rate. Since market costs for digital forms of money depend on market interest, the cost of a specific digital money in contrast with the other Crypto changes generally, As each Crypto coin has its upsides and downsides and as the cost of the coin is differing generally as for the trade paces of coin and the fame of the Crypto coins in public. The work proposes to foresee the future costs of Cryptographic forms of money like Ethereum and Binance Coin utilizing AI draws near. The result of the proposed work is to gauge the costs of Ethereum and Binance Coin and ubiquity in the coming years

MangiReddiHemanth, MunipalliSasi Chandra, VaddeRaviteja, R. Sumathi · 5 authors

The major aim of this work is to uncover the accuracy of the Bitcoin price in any fiat/flat currency that can be predicted in advance. Bitcoin is a form of cryptocurrency and is now one of the most popular types of investments in the stock market. And bitcoin is the only form of cryptocurrency that has been on the rise in the last few years, and sometimes a sudden collapse without knowing the impact behind it in the stock market. To utilize the long short-term memory for predicting the bitcoin value in advance. Many researchers used RNN for this bitcoin prediction and observed that it lacks in consistency, to overcome this issue LSTM and ARIMA are used to ensure the accuracy and yields better prediction in terms of time series and proves that it is superior to existing state of art techniques.

Meiryani Meiryani, Caineth Delvin Tandyopranoto, Jason Emanuel, A. S. L. Lindawati · 7 authors

This study aims to determine the effect of global price movements for energy sector commodities, especially Crude Oil and Natural Gas Prices, on cryptocurrency price movements. This study focuses more on the Bitcoin cryptocurrency. This study uses quantitative methods, and the data collection used is secondary data with weekly data and the period from January 1, 2020-July 31, 2021. The number of observations used in this study amounted to 79 observations. Secondary data sources are obtained through the website finance.yahoo.com. The data processing technique will be carried out using Stata and SPSS software, the Multiple Linear Regression method, and the Classical Assumption Test. The results of this study show that global prices for energy sector commodities, especially Crude Oil, Natural Gas, have a positive effect on Bitcoin price movements. These results indicate a link between energy and Bitcoin caused by Bitcoin miners who are mining Bitcoin using energy so that when the price of Bitcoin rises, the price of energy will also increase.

Μιχαήλ Κανελλόπουλος

Being a revolutionary form of financial instrument, Bitcoin's rapid price fluctuations inevitably prompt the query of whether its price can be predicted. This is a crucial question, particularly in light of Bitcoin's brief history and the ease with several factors may have an impact on its price. This study investigates the direction prediction of Bitcoins volatility using internal, blockchain data, such as the past prices of Bitcoin and blockchain’s characteristics. Convolutional neural networks (CNN), among other methodologies, have lately been used for automatic feature selection and market forecasting. In this research, we propose a CNN based framework using blockchain features for predicting the direction of Bitcoins volatility from a set of data from various sources. The proposed methodology has been used to predict the next day's direction of movement for Bitcoin on a variety of different variables. The evaluation displays a significant accuracy of 57% in prediction's performance and a mean absolute error (loss) of 43%, a result that suggests CNN's framework significant when predicting Bitcoin's volatility. This research proposes an interpretative approach to derive feature importance, which represents the degree to which an input feature may discriminate between distinct classes, in order to better understand how these networks make their final selections. Moreover, we found that the blockchain’s characteristics that had an impact in the performance of the CNN algorithm were the total value of all transaction outputs per day, the miner’s revenue divided by the number of transactions, the total estimated value in USD of transactions, the miner’s revenue and the total number of confirmed transactions per day.

Swapnil Sonawane, Dilip Motwani

No abstract is available for this record.

M. Darshan, S.R Raswanth, Priyanka Kumar

In recent times, the rise of Non Fungible Tokens has been inevitable. An NFT is a special kind of cryptographic token that represents the ownership of a unique piece of digital property. They are tamper-proof due to the use of distributed public ledger that records verified information across a network of computers. With the rise of crypto-trading, the NFT market segment has seen an accumulation in its trading volume in the capital market. There are various factors determining the price and sales of the NFTs and there is a need for meaningful insights from the data generated intermittently over time. With this as motivation, this proposed research work explores the factors that create an impact on the NFT market and in-depth data analysis with the help of brokerage firm data. This proposed work helps NFT enthusiasts would be able to derive the correlation between cryptocurrency market and NFT market.

Shuhui Zhang, Tian Lan, Lianhai Wang, Shujiang Xu · 5 authors

Ethereum, a typical application of blockchain technology, has attracted extensive attention from all walks of life since its release. Owing to imperfections in existing supervision technology, illegal and criminal activities on blockchain platforms are becoming increasingly frequent. The most typical Ethereum fraud is the Ponzi scheme, which causes blockchain investors to lose millions of assets and severely impacts social development. Currently, Ponzi scheme detection primarily focuses on machine learning and data mining. However, existing detection methods still have two problems in data imbalance processing and feature extraction: (1) data enhancement using an oversampling algorithm produces noise and (2) feature redundancy existing in extracted feature data. The SMOTEENN algorithm is introduced to solve data imbalance. The PD-SECR method, the Convolutional Neural Network (CNN) feature extraction, and random forest (RF) classification models are used for detection, but the two models are independently trained. The results show that the detection method proposed in this study is more suitable for the Ethereum Ponzi scheme.

Rohan Shinde, Sahil Chorghe, Keval Dhanani, Abhijeet Salunke

In today's digital world there are a lot of issues in verifying the legitimacy of documents that an individual provides such as School/College mark sheets, Course certificates, etc. Companies are concerned whether a certificate provided to them is legit or forged. Verifying these documents manually or by offline visits is a tedious procedure and takes days to complete. The use of physical documents is also inconvenient and inefficient in today's fast paced digital world. In this paper, to counter this forgery issue a platform using blockchain technology is suggested. Blockchain provides immutability, once the document is stored in the blockchain it cannot be changed. An overview of the working and implementation of our platform and the functionalities provided are presented.

Moudher Kh. Abdal-Hammed, Afraa Ghazal, Hendra Ibrahim, Akhil Ahmed

redicting currency rates is important, for everyone who is trading and trying to build an investment portfolio from a range of crypto currencies. It is not subject to the same restrictions as fiat currencies. In this study, we seek to predict the exchange rate of BIT-COIN against the US dollar. The short-term data (365 observations) is processed using the LSTM model as one of the neural network models. Modeling is conducted by training a sample size of 67%, taking into account sharp fluctuations in the price of trade and a certain level of market efficiency. The GARCH model is used to select appropriate historical periods for how the LSTM model works and to test proficiency at the weak, semi-strong, and strong levels. The data series obtained from the website (Investing.com) have been processed. The researchers have found that the performance of the neural network improves as the EPOCH value increases with a training (research) period of 50 days before, which is consistent with the results of the proficiency test at the weak level. It agrees with the results of the sufficiency test at the weak level, which indicates that in the case under study (the Bitcoin market is effective at the weak level). It is advised that crypto-currency investors rely more on the historical trend of the price of the currency than on its current price, taking advantage of the artificial neural network model (LSTM) in dealing with little data of high volatility.