Blockchain Papers

Yuxin Zhang, Rajiv Garg, Linda L. Golden, Patrick L. Brockett · 5 authors

No abstract is available for this record.

Jingben Lu, Yawei Song, Qianhui Li, Junrong Tang · 5 authors

In recent years, machine learning has achieved good results in the field of asset prices. Compared with traditional data analysis and technical analysis, using machine learning methods can show unique advantages in various aspects. In this paper, we combine the correlation between bull and bear mark

Dubey Anand, Siddhartha Choubey

Blockchain technology has emerged as a revolutionary distributed ledger system with the potential to transform various industries, including finance, supply chain, healthcare, and more. However, the decentralized nature of blockchain introduces unique challenges in terms of fraud detection and prevention. This abstract provides an overview of the current state of research and technologies related to fraud detection in blockchain technology-based systems. The paper begins by discussing the fundamental characteristics of blockchain, highlighting its immutability, transparency, and decentralization. These characteristics provide a promising foundation for ensuring data integrity and security but also pose significant challenges in detecting and mitigating fraudulent activities. Next, the paper explores various types of fraud that can occur in blockchain systems, such as double-spending, Sybil attacks, 51% attacks, smart contract vulnerabilities, and identity theft. Each type of fraud is explained along with its potential impact on the integrity and reliability of blockchain systems. To address these challenges, the paper presents an overview of existing fraud detection techniques in blockchain systems. These techniques encompass a range of approaches, including anomaly detection, machine learning algorithms, consensus mechanisms, cryptographic techniques, and forensic analysis. The strengths and limitations of each technique are discussed to provide a comprehensive understanding of their applicability in different scenarios. Furthermore, the paper highlights emerging trends in fraud detection research within the blockchain domain. These trends include the integration of artificial intelligence and blockchain technology, the use of decentralized and federated machine learning approaches, the development of privacy-preserving fraud detection mechanisms, and the utilization of data analytics and visualization techniques for improved detection and investigation. The paper concludes by emphasizing the importance of continuous research and development in fraud detection for blockchain technology-based systems. As blockchain adoption expands across industries, it is crucial to enhance the security and trustworthiness of these systems by effectively detecting and preventing fraud. Future directions for research and potential challenges are also discussed, encouraging further exploration in this vital area of study.

Wenbing Zhao, Izdehar M. Aldyaflah, Pranav Gangwani, Santosh Joshi · 6 authors

In this paper, we present the design, implementation, and evaluation of a secure sensing data processing and logging system. The system is inspired and enabled by blockchain. In this system, a public blockchain is used as immutable data store to store the most critical data needed to secure the system. Furthermore, several innovative blockchain-inspired mechanisms have been incorporated into the system to provide additional security for the system’s operations. The first priority in securing sensing data processing and logging is admission control,i.e., only legitimate sensing data are accepted for processing and logging. This is achieved via a sensor identification and authentication mechanism. The second priority is to ensure that the logged data remain intact overtime. This is achieved by storing a small amount of data condensed from the raw sensing data on a public blockchain. A Merkel-tree based mechanism is devised to link the raw sensing data stored off-chain to the condensed data placed on public blockchain. This mechanism passes the data immutability property of a public blockchain to the raw sensing data stored off-chain. Third, the raw sensing data stored off-chain are secured with a self-protection mechanism where the raw sensing data are grouped into chained blocks with a moderate amount of proof-of-work. This scheme prevents an adversary from making arbitrary changes to the logged data within a short period of time. Fourth, mechanisms are developed to facilitate the search of the condensed data placed on the public blockchain and the verification of the raw sensing data using the condensed data placed on the public blockchain. The system is implemented in Python except the graphical user interface, which is developed using C#. The functionality and feasibility of the system have been evaluated locally and with two public blockchain systems, one is the IOTA Shimmer test network, and the other is Ethereum.

Rohan Maheshwari, Sriram Praveen V A, G Shobha, Jyoti Shetty · 6 authors

A key motivator for the usage of cryptocurrency such as bitcoin in illicit activity is the degree of anonymity provided by the alphanumeric addresses used in transactions. This however does not mean that anonymity is built into the system as the transactions being made are still subject to the human element. Additionally, there is around 400 Gigabytes of raw data available in the bitcoin blockchain, making it a big data problem. HPCC Systems is used in this research, which is a data intensive, open source, big data platform. This paper attempts to use timing data produced by taking the time intervals between consecutive transactions performed by an address and make an identification of the nature of the address (illegal or legal). With the use of three different goodness of fit run tests namely Kolmogorov–Smirnov test, Anderson-Darling test and Cramér–von Mises criterion, two addresses are compared to find if they are from the same source. The BABD-13 dataset was used as a source of illegal addresses, which provided both references and test data points. The research shows that time-series data can be used to represent transactional behaviour of a user and the algorithm proposed is able to identify different addresses originating from the same user or users engaging in similar activity.

M. Ramalingam, G. Chemmalar Selvi, Nancy Victor, Rajeswari Chengoden · 11 authors

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.

Habeeba Tabassum Shaik, Bipin Kumar, Bhasha Pydala

No abstract is available for this record.

Meng Huang, Jia Yang, Cong Liu

No abstract is available for this record.

P. V. Nagamani, Gowri Anand, Srinivasa Prasanna, Basava Raju · 5 authors

The past several years have seen an increase in interest in trading that is supported by machine learning and artificial intelligence.Utilize automated trading with the aid of machine learning and artificial intelligence to reap the maximum rewards from the cryptocurrency market.For a specific time, we keep the daily data.We achieve excellent results by utilising tactics supported by cutting-edge algorithms.The results produced the expansion in the crypto currency industry with the aid of straight forward architecture and algorithms.The rise in market capitalization has led to a rise in popularity for the cryptocurrency in 2017.Today's market involves more than 1500 crypto currencies.For usage in online transactions, the crypto currency can be created.A crypto money technology is bitcoin.Bitcoin's value changes constantly, second by second.As a result, we apply machine learning architecture to forecast the value of the bitcoin price in this case.We are working to demonstrate that, in comparison to previous techniques and architectures, this ML architecture produces results that are more accurate.Our study use the Support Vector Machine(SVM) and K Nearest Neighbor(KNN)algorithms to successfully forecast bitcoin prices.The findings demonstrate that the Support Vector Machine(SVM) method outperforms the K Nearest Neighbor(KNN) method as it is currently being used.

Zeliha Can Ergün, Büşra KUTLU KARABIYIK

No abstract is available for this record.

Kunika Mathur, K Nandini

Managing and storing data are crucial tasks for any industry, since they require accurate and secure record-keeping. Using blockchain technology has significantly increased recently due to its capability to address some of the challenges that come with traditional data storage and management systems. In this paper, we will explore the use of Ethereum smart contracts powered by the Solidity programming language using AES-CMAC.

M. Thamban Nair, Mohamed I. Marie, Laila A. Abd-Elmegid

One of the most significant and extensively utilized cryptocurrencies is Bitcoin (BTC). It is used in many different financial and business activities. Forecasting cryptocurrency prices are crucial for investors and academics in this industry because of the frequent volatility in the price of this currency. However, because of the nonlinearity of the cryptocurrency market, it is challenging to evaluate the unique character of time-series data, which makes it impossible to provide accurate price forecasts. Predicting cryptocurrency prices has been the subject of several research studies utilizing machine learning (ML) and deep learning (DL) based methods. This research suggests five different DL approaches. To forecast the price of the bitcoin cryptocurrency, recurrent neural networks (RNN), long short-term memories (LSTM), gated recurrent units (GRU), bidirectional long short-term memories (Bi-LSTM), and 1D convolutional neural networks (CONV1D) were used. The experimental findings demonstrate that the LSTM outperformed RNN, GRU, Bi-LSTM, and CONV1D in terms of prediction accuracy using measures such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score (R2). With RMSE= 1978.68268, MAE=1537.14424, MSE= 3915185.15068, and R2= 0.94383, it may be considered the best method.

Karamath Ateeq, Ahmed Abdelrahim Al Zarooni, Abdur Rehman, Muhammad Adnan Khan

Researchers and investors have recently become interested in forecasting the cryptocurrency price forecasting but the most important currency can take that it’s the bitcoin exchange rate. Some researchers have aimed at leveraging the technical and financial characteristics of Bitcoin to create predictive models, while others have utilized conventional statistical methods to explain these factors. This article explores the LSTM model for forecasting the value of bitcoins using historical bitcoin price series. Predict future bitcoin prices by developing the most accurate LSTM forecasting model, building an advanced LSTM forecasting model (LSTM-BTC), and comparing past bitcoin prices. This is the second step, if looking at the end of the model, it has very high accuracy in predicting future prices. The performance of the proposed model is evaluated using five different datasets with monthly, weekly, daily, hourly, and minute-by-minute bitcoin price data with total records from January 1, 2021, to March 31, 2022. The results confirm the better forecasting accuracy of the proposed model using LSTM-BTC. The analysis includes square error MSE, RMSE, MAPE, and MAE of bitcoin price forecasting. Compared to the conventional LSTM model, the suggested LSTM-BTC model performs better. The contribution made by this research is to present a new framework for predicting the price of Bitcoin that solves the issue of choosing and evaluating input variables in LSTM without making firm data assumptions. The outcomes demonstrate its potential use in applications for industry forecasting, including different cryptocurrencies, health data, and economic time.

Aleksandar Petrović, Luka Jovanović, Miodrag Živković, Nebojša Bačanin · 6 authors

The interest for cryptocurrencies is high and hence this work focuses on providing a practical real-world application of the swarm metaheuristics and long short term memory model (LSTM).The goal is price forecasting which is interesting due to the high volatility of the cryptocurrencies.The authors apply LSTM for the solution of the problem which has been proven to reap results with this type of problem.The LSTM is further optimized by a swarm metaheuristic -arithmetic optimization algorithm (AOA).The solution was tested alongside familiar high-performing competitors with the use of standard metrics mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).These metrics have been used for comparison between the solutions, upon which the proposed solution obtained overall best performance that testifies to the improvement of the solution.

João Crisóstomo, Victor Lobo, Fernando Bação

No abstract is available for this record.

Gagandeep Kaur, Ruchika Bindal, Veerpal Kaur, Inderpreet Kaur · 5 authors

No abstract is available for this record.

Hae Sun Jung, Seon Hong Lee, Haein Lee, Jang Hyun Kim

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts. An accuracy of 90.57% and an area under the receiver operating characteristic curve value (AUC) of 97.48% were obtained using the extreme gradient boosting (XGBoost). It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner (VADER) and 11 technical indicators utilizing moving average, relative strength index (RSI), stochastic oscillators in predicting Bitcoin price trends can produce significant results. Thus, the input features used in the paper can be applied on Bitcoin price prediction. Furthermore, this approach allows investors to make better decisions regarding Bitcoin-related investments.

Ashish Khanna, Devansh Singh, Ria Monga, Tarun Kumar · 6 authors

No abstract is available for this record.

Hasan Isa Jaafar Ebrahim, Husain Mohamed Ali Alaswad, Sayed Mohamed Fadhul, Ruaa Binsaddig

No abstract is available for this record.

Vaishali Ravindranath, M. K. Nallakaruppan, M. Lawanya Shri, Balamurugan Balusamy · 5 authors

No abstract is available for this record.

Maliha Zahan Chowdhury, Asaduzzaman Asaduzzaman

Due to the ease with which false documents can be produced, strict verification and authentication procedures for some original documents may be required. A well-crafted fake document is never easily identified. As a result, a secure method of document verification is crucial. It is possible to cut costs and reduce document verification time from days to a few seconds by utilizing blockchain technology. Blockchain is a decentralized peer-to-peer network that provides security, dependability, authenticity, immutability, and transparency. It is resistant to modifications by design. This work involves developing a complete decentralized blockchain-based document verification system for multiple organizations. The project is built on the Ethereum public blockchain, which makes it flexible and suited for cooperation between various parties. We have used the InterPlanetary File System (IPFS) to store crucial documents. IPFS uses a decentralized protocol, which helps us achieve our goal of making a completely decentralized system. The backend of the system is a website developed on the React framework. The developed system is for multiple organizations. It has an admin verification system, which includes a payment system. Multiple organizations can control their separate platforms with different verifier panels from their own organizations. Only the admin can add verifiers to the system. He/she can choose suitable candidates from all the profiles created by that organization and give the person the authority to check the authenticity of the uploaded documents. The Admin adds a verifier with the person’s wallet address. The uploaded documents get checked by authorized verifiers. The status of the document, whether it is an authentic document or not, is determined by the votes of 50% of the verifiers. This system can verify the authenticity of documents of all kinds more securely than any other conventional system.

Yurii Kleban, Tetiana Stasiuk

The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naïve model, the traditional ARIMA model, and the FB Prophet results.

Abdilcelil Koç, Ali Çeli̇k

Çalışmanın amacı, 03.01.2020 ile 28.02.2022 dönemi için üretim araçlarındaki gelişmenin bir başka veçhesi olan dijitalleşme ile kripto paralara yönelimin hızlanmasının geleneksel borsalara alternatif olup olmayacağını simetrik ve asimetrik nedensellik test yöntemleriyle incelemektir. Bu çerçevede simetrik nedensellik analiz sonuçlarına göre, BTC ve ETH fiyatlarından SP500, NASDAQ ve DOWJ fiyatlarına doğru bir nedensellik ilişkisi saptanmış, aynı zamanda VIX’ten BTC ve ETH’ye doğru bir nedensellik ilişkisi bulunmuştur. Asimetrik nedensellik analizi sonuçlarına göre SP500, NASDAQ, DOWJ ve Altın fiyatlarındaki negatif değişmelerden, BTC fiyatlarındaki pozitif değişmelere doğru bir nedensellik ilişkisi tespit edilmişken, NASDAQ ve DOWJ fiyatlarındaki pozitif değişmelerden ETH fiyatlarının pozitif değişmelerine doğru bir nedensellik ilişkisinin varlığına ulaşılmıştır. Son olarak kripto paralar arasındaki nedensellik ilişkisi sınandığında BTC fiyatlarındaki negatif değişimlerden ETH fiyatlarındaki pozitif değişimlere, ETH fiyatlarındaki negatif değişimlerden BTC fiyatlarındaki pozitif değişimlere doğru bir nedensellik ilişkisi tespit edilmiştir.

Muhammad Zakhwan, Mohamed Rafik, Noraisyah Mohamed Shah, Anis Salwa Binti Mohd Khairuddin

Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognised as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency in-vestments, many Alternative Coins (Altcoins) resurfaced as to mimic the fiat currency. Altcoins prediction, as the name suggests the alternative coins from the traditional cryptocurrency which is Bitcoin (BTC). There are several methods to forecast cryptocurrency prices namely Technical Analysis and Fundamental Analysis which has been widely used in forecasting fiat and stock prices. With the emergence of Artificial Intelligence (AI), Machine Learning and Deep Learning algorithms provide a different perspective on how investors can estimate the trend or the movement of prices. In this thesis, as cryptocurrency price are time-dependent, Recur-rent Neural Network (RNN) is presented due to RNN’s nature that is well suited for Time Series Analysis (TSA). The topology of proposed RNN model consists of 3 stages which are model groundwork, model development and testing and optimisation. The RNN architecture are extended to two different models specifically Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU). There are 4 hyperparameters that will affect the accuracy of the deep learning model in predicting cryptocurrency price. Hyperparameters tuning set the basis of optimising the model to improve the accuracy of cryptocurrency prediction. Hyperparameters listed in this project are limited to number of epochs, adaptive optimisation algorithm, dropout rate, and batch size. Next, the models are tested with data of different coins listed in the cryptocurrency market with different input features to find out the effect on the accuracy and robustness of the model in predicting the cryptocurrency price. This research demonstrates that GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Executional Time, scoring 2.2201, 0.8076 and 200s using intra-day trading strategy Open, High, Low, Close Price (OHLC) as input features.