Blockchain Papers

Jonas Gehrlein, Grzegorz Miebs, Matteo Brunelli, Miłosz Kadziński

No abstract is available for this record.

Yingjie Zhu, Jiageng Ma, Fangqing Gu, Jie Wang · 10 authors

Bitcoin is one of the most successful cryptocurrencies, and research on price predictions is receiving more attention. To predict Bitcoin price fluctuations better and more effectively, it is necessary to establish a more abundant index system and prediction model with a better prediction effect. In this study, a combined prediction model with twin support vector regression was used as the main model. Twenty-seven factors related to Bitcoin prices were collected. Some of the factors that have the greatest impact on Bitcoin prices were selected by using the XGBoost algorithm and random forest algorithm. The combined prediction model with support vector regression (SVR), least-squares support vector regression (LSSVR), and twin support vector regression (TWSVR) was used to predict the Bitcoin price. Since the model’s hyperparameters have a great impact on prediction accuracy and algorithm performance, we used the whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) to optimize the hyperparameters of the model. The experimental results show that the combined model, XGBoost-WOA-TWSVR, has the best prediction effect, and the EVS score of this model is significantly better than that of the traditional statistical model. In addition, our study verifies that twin support vector regression has advantages in both prediction effect and computation speed.

Yizhi Luo, Jianhui Zhang

Stale blocks are not avoidable in blockchain, such as the Bitcoin network, when proof-of-work is used as the consensus protocol. However, as the economic loss to the miners and the security risk to the network cannot be ignored, research is needed to identify and analyse stale blocks. By analysing the factors influencing the generation of stale blocks, the authors propose a new machine learning model based on XGBoost. They propose a new data collection method for bitcoin nodes to obtain real data for training prediction model. Then, based on the model, they generate optimal mining strategies and analyse the economic benefits. The experimental data and application cases show that the real-time data detection and machine learning model that they propose can accurately identify and predict the generation of stale blocks and generate an economically optimal mining strategy in the Bitcoin network with the presence of stale blocks.

Marwan Abdul Hameed Ashour, Ammar Sh. Ahmed

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combina-tion of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The sec-ond key discovery was that a strong combination forecasting model that responds to the multiple fluctua-tions that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction accuracy criterion and matching curve-fitting in this work demonstrated that if the residuals of the revised model are white noise, the forecasts are unbiased. Future work investigating robust hybrid model forecasting using fuzzy neural networks would be very interesting.

Víctor Abraham Villagrá González, Luı́s Sánchez, Jorge Lanza, Juan Ramón Santana · 6 authors

As data becomes the new fuel of the economy and a key asset to address our societal challenges, we cannot afford to have the data of businesses, public sector and individuals stored and kept unexploited or, what is worse, exploited by others that actually have the resources and capacity to do it. This is affecting not only our economic performance but also our security, safety and sovereignty. Among the plethora of data sources that exist nowadays, the Internet of Things (IoT) is recognised as a game-changer technology that expands its applicability to a huge variety of domains. Its main asset is, precisely, the data that the myriad of sensors embedded in the environment are constantly generating. The diffusion of platforms for IoT data sharing and monetisation is one of the key success factors which may help to drive the data economy and industrial transformation. In this paper, we are presenting a data sharing platform based on Blockchain, so-called Blockchain-based IoT Data Marketplace (BIDM) over which data producers and data consumers are able to share data in a decentralised and trustworthy manner. The BIDM enables a data marketplace where owners of IoT infrastructures can expose the observations that their devices generate while retaining control over who accesses each observation and directly getting revenues according to the price they have set. The evaluation that we have carried out of the BIDM’s behaviour and performance in terms of operational execution times and scalability has been the basis for the discussion that we are presenting on the shortcomings that are typically associated with the use of Blockchain technologies as enablers for data marketplaces. This discussion also includes the evaluation of the challenges that must be considered for the creation of secure and interoperable Data Spaces based on Blockchain.

Mohamed Moetez Abdelhamid, Layth Sliman, Raoudha Ben Djemaa, Boussad Ait Salem

Due to its features regarding immutability, transparency and traceability, blockchain has emerged as a promising solution in various application domains. However, despite its advantages, blockchain suffers from major drawbacks in terms of scalability and performance. This is due to blockchain’s ever-growing nature. Several research studies focus on optimizing blockchain by adopting new techniques and solutions such as pruning, sharding, SegWit and off chain storage solutions. However, so far, most of the suggested solutions entail massive technical burden, increase complexity and reduce transparency and security. In this work, after conducting a comparison among existing blockchain pruning solutions, we present a new intelligent pruning technique that only prunes spent transactions or previous versions of smart contracts. In the proposed approach, data are pruned according to data usage, allowing each participant to independently provide a data list that should be pruned. Our solution involves an intelligent selection process based on particle swarm optimization that uses node parameters to select the best list from all agents lists, this list will be adopted by all the blockchain network members. The approach is implemented in our ABISchain blockchain. ABIshchain pruning solution will reduce the storage requirements for nodes, increase the overall performance and security level as joining nodes will need only to fetch a pruned blockchain (which is more secure than joining blockchain network with a blockchain snapshots provided by other nodes).

I.sibel KERVANCI, Fatih AKAY

Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they aren't specific for all problems. In this paper Long Short-Term Memory (LSTM), eight different hyperparameters (go-backward, epoch, batch size, dropout, activation function, optimizer, learning rate and, number of layers) were used to examine to daily and hourly Bitcoin datasets. The effects of each parameter on the daily dataset on the results were evaluated and explained These parameters were examined with hparam properties of Tensorboard. As a result, it was seen that examining all combinations of parameters with hparam produced the best test Mean Square Error (MSE) values with hourly dataset 0.000043633 and daily dataset 0.00073843. Both datasets produced better results with the tanh activation function. Finally, when the results are interpreted, the daily dataset produces better results with a small learning rate and small dropout values, whereas the hourly dataset produces better results with a large learning rate and large dropout values.

Piergiuseppe Di Pilla, Remo Pareschi, Francesco Salzano, Federico Zappone

Introduction In recent years, software ecosystems have become more complex with the proliferation of distributed systems such as blockchains and distributed ledgers. Effective management of these systems requires constant monitoring to identify any potential malfunctions, anomalies, vulnerabilities, or attacks. Traditional log auditing methods can effectively monitor the health of conventional systems. Yet, they run short of handling the higher levels of complexity of distributed systems. This study aims to propose an innovative architecture for system auditing that can effectively manage the complexity of distributed systems using advanced data analytics, natural language processing, and artificial intelligence. Methods To develop this architecture, we considered the unique characteristics of distributed systems and the various signals that may arise within them. We also felt the need for flexibility to capture these signals effectively. The resulting architecture utilizes advanced data analytics, natural language processing, and artificial intelligence to analyze and interpret the various signals emitted by the system. Results We have implemented this architecture in the DELTA (Distributed Elastic Log Text Analyzer) auditing tool and applied it to the Hyperledger Fabric platform, a widely used implementation of private blockchains. Discussion The proposed architecture for system auditing can effectively handle the complexity of distributed systems, and the DELTA tool provides a practical implementation of this approach. Further research could explore this approach's potential applications and effectiveness in other distributed systems.

Sandi Gec, Vlado Stankovski, Dejan Lavbič, Petar Kochovski

IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts' correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system's algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts' development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.

Shuo Qian, Yinglai Qi

Machine learning has a wide range of applications to meet the complexity of data and various expectations for prediction types.In this study, a comprehensive review of various machine learning approaches for Bitcoin price prediction will be proposed.After examining previous research on cryptocurrency prediction using Long-Short Term Memory (LSTM), Multi-layer Perceptions (MLP), and Support Vector Machine (SVM), with the focus on LSTM, it can be found that LSTM is a widely employed method in Bitcoin price prediction because of its advantages in incorporating both long-term and short-term dependencies.This paper reviews a series of research papers by comparing the differences between the methods they implemented, to a limited extent, based on their predictive power, replicability, and model limitations.Furthermore, some potential improvements and explored innovations for future studies also be discussed.

B. Bhavya Likhitha, Chahat Raj, Mir Salim Ul Islam

Cryptocurrency has emerged as a revolutionary innovation that has been replacing traditional finances and enthralling the worldwide technology landscape. This has gained a lot of popularity worldwide for its potential to enable peer-to-peer transactions and offer opportunities for investment and novelty. Nevertheless, it gives rise to issues concerning regulatory adherence, instability, and security apprehensions, turning them into a topic of continuous evaluation and investigation within the fields of finance and technology. This research paper presents a comprehensive exploration of the historical evolution of “Ethereum” as one of the leading blockchain platforms, with a primary focus on price prediction using a long-short-term memory (LSTM) machine learning model. The study includes various critical aspects of Ethereum, starting from its historical evolution to its potential future scope in scaling solutions and payments, and also covering the insights of Ethereum’s tokenomics, utility, and beyond. In addition, the methodology involves using the LSTM model to analyze data from Ethereum. The accuracy of price predictions is assessed by evaluating error metrics and further improved by visualizing the data through graphs that show indicators. This paper gives an in-depth perspective for anyone who is seeking a holistic understanding of cryptocurrencies, mainly concentrated on Ethereum, and also provides valuable guidance to investors, developers, and enthusiasts, encouraging them to make knowledgeable decisions in the ever-changing blockchain ecosystem.

Khaled Almiani, Young Choon Lee, Tawfiq Alrawashdeh, Amirmohammad Pasdar

The usage of blockchain technology has been significantly expanded with smart contracts and blockchainoracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often ‘short-sighted’ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly. this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources.

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.

Abebe Diro, Lu Lu Zhou, Akanksha Saini, Shahriar Kaisar · 5 authors

No abstract is available for this record.

Zhiyuan Li, En-Han He

In recent years, with the rapid development of the digital economy, digital currencies such as Bitcoin and Ethereum have become increasingly popular among the public. Tracking and regulating digital currency transactions have become a challenging technology for the healthy development of the digital economy. That is because blockchain and peer-to-peer networks are the underlying technologies of digital currencies. Blockchain transaction has some new features, such as stronger anonymity and distributed storage. Therefore, it is difficult for the regulatory system to track the transaction relationships among users. Recent studies have shown that the accuracy, time, and space costs of transaction tracking, as well as the trade-offs between them, still need to be improved. In this article, we propose a new blockchain transaction tracking model called BT2(Bitcoin Transaction Tracking Model). BT2first combines an improved sampling aggregation algorithm with a graph neural network. And then, it exploits an inductive aggregation method to effectively generate rich node embeddings for a small number of unobserved nodes. Next, the node embedding vectors are used to generate edge information among nodes through message-passing functions. Finally, we can use the edge information to obtain the relations among Bitcoin accounts. This paper evaluates the model on the real-world dataset and explores the impact of various parameters, such as network depth and iteration time, etc. From the experimental results, the model’s average AUC (area under the ROC curve) can reach up to 0.93, and the average accuracy is 86%. The numerical results indicate that the performance of BT2is better than the state of art methods.

Stefan Paulus, Benjamin Leiding

No abstract is available for this record.

Junho Kim, Hanul Sung

Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate predictions. In this paper, we analyze the prediction performance trends under various hyperparameter configurations to help them identify the optimal hyperparameter combination with little effort. We employ two datasets which have different time periods with the same bitcoin price to analyze the prediction performance based on the similarity between the data used for learning and future data. With them, we measure the loss rates between predicted values and real price by adjusting the values of three representative hyperparameters. Through the analysis, we show that distinct hyperparameter configurations are needed for a high prediction accuracy according to the similarity between the data used for learning and the future data. Based on the result, we propose a direction for the hyperparameter optimization of the bitcoin price prediction showing a high accuracy.

Raul Cristian Bag, Bruno Spilak, Julian Winkel, Wolfgang Karl Härdle

Living in the Information Age, the power of data and correct statistical analysis has never been more prevalent. Academics and practitioners require nowadays an accurate application of quantitative methods. Yet many branches are subject to a crisis of integrity, which is shown in an improper use of statistical models, $p$-hacking, HARKing, or failure to replicate results. We propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain network, Quantinar (quantinar.com), to support quantitative analytics knowledge paired with code in the form of Quantlets (quantlet.com) or software snippets. The integration of blockchain technology makes Quantinar a decentralized autonomous organization (DAO) that ensures fully transparent and reproducible scientific research.

Sukrutha L. T. Vangipuram, Saraju P. Mohanty, Elias Kougianos, Chittaranjan Ray

Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional techniques for forecasting groundwater availability by conducting research and studies. One challenging step for this type of study is collecting groundwater data from different sites and securely sending it to the nearby edges without exposure to hacking and data tampering. In the current paper, we send raw data formats from the Internet of Things to the Distributed Data Storage (DDS) and Blockchain (BC) edges. We use a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts. This work demonstrates a modern and innovative approach combining DDS and BC technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations. We have shown performance improvements with increased data quality and integrity.

Hangwei Feng, Jinlin Wang, Yang Li

In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data in third-party storage systems to reduce the node storage pressure. The new blockchain storage method brings the blockchain transaction retrieval problem. The problem is that when unable to locate the block containing this transaction, the user must fetch the entire blockchain ledger data from the third-party storage system, resulting in huge communication overhead. For this problem, we exploit the semi-structured data in the blockchain and extract the universal blockchain transaction characteristics, such as account address and time. Then we establish a blockchain transaction retrieval system. Responding to the lacking efficient retrieval data structure, we propose a scalable secondary search data structure BB+ tree for account address and introduce the I2B+ tree for time. Finally, we analyze the proposed scheme’s performance through experiments. The experiment results prove that our system is superior to the existing methods in single-feature retrieval, concurrent retrieval, and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is decreased by 43.16% under the multi-feature hybrid retrieval. It has better stability in different block sizes and concurrent retrieval scales.

Jatin Nainani, Nirman Taterh, Md Ausaf Rashid, Ankit Khivasara

For a long time predicting, studying and analyzing financial indices has been of major interest for the financial community. Recently, there has been a growing interest in the Deep-Learning community to make use of reinforcement learning which has surpassed many of the previous benchmarks in a lot of fields. Our method provides a feature rich environment for the reinforcement learning agent to work on. The aim is to provide long term profits to the user so, we took into consideration the most reliable technical indicators. We have also developed a custom indicator which would provide better insights of the Bitcoin market to the user. The Bitcoin market follows the emotions and sentiments of the traders, so another element of our trading environment is the overall daily Sentiment Score of the market on Twitter. The agent is tested for a period of 685 days which also included the volatile period of Covid-19. It has been capable of providing reliable recommendations which give an average profit of about 69%. Finally, the agent is also capable of suggesting the optimal actions to the user through a website. Users on the website can also access the visualizations of the indicators to help fortify their decisions.

Zheng Xu, Liu Ji

The traditional identification method of the digital bond trading contract system is single chain identification. Due to a large number of digital bond trading users, single chain identification will affect the performance of the system in the trading process. Therefore, we aim to propose a digital bond trading contract system based on blockchain technology and study the system architecture. In this paper, the architecture of the bond trading system is optimized by using the technology of blockchain, and the software architecture is developed by using the technology of blockchain. Through the performance test of the system, the advantages of the system architecture in practical application are verified. Firstly, the overall hardware architecture of the system is designed, and the internal structure of the controller in the control layer is redeployed. Combining the Mork tree and Patricia tree, the data structure based on blockchain and the block header data structure of the transaction contract system are optimized. By analyzing the operation process of the smart contract, the process of the smart contract consensus algorithm is optimized. The system performance test results show that the performance of the designed system in system transaction throughput, transaction delay, and system security is better than the traditional system, which verifies the effectiveness and reliability of the designed system.

Martin Kolář

Research and applications in Machine Learning are limited by computational resources, while 1% of the world's electricity goes into calculating 34 billion billion SHA-256 hashes per second, four orders of magnitude more than the 200 petaflop power of the world's most powerful supercomputer. The work presented here describes how a simple soft fork on Bitcoin can adapt these incomparable resources to a global distributed computer. By creating an infrastructure and ledger fully compatible with blockchain technology, the hashes can be replaced with stochastic optimizations such as Deep Net training, inverse problems such as GANs, and arbitrary NP computations.

Chetan Sharma, Shamneesh Sharma, Sakshi Sakshi

No abstract is available for this record.