Alex Kaplunovich, Karuna Pande Joshi, Yelena Yesha
While adopting Blockchain technologies to automate their enterprise functionality, organizations are recognizing the challenges of scalability and manual configuration that the state of art present. Scalability of Hyperledger Fabric is an open challenge recognized by the research community. We have automated many of the configuration steps of installing Hyperledger Fabric Blockchain on AWS infrastructure and have benchmarked the scalability of that system. We have used the UCR (University of California Riverside) Time Series Archive with 128 timeseries datasets containing over 191,177 rows of data totaling 76,453,742 numbers. Using an automated Serverless approach, we have loaded this dataset, by chunks, into different AWS instances, triggering the load by SQS messaging. In this paper, we present the results of this benchmarking study and describe the approach we took to automate the Hyperledger Fabric processes using serverless Lambda functions and SQS triggering. We will also discuss what is needed to make the Blockchain technology more robust and scalable.
In this study, a trusted holistic abstraction is proposed and analytically discussed using universal scalability law and Markovian chain Monte-Carlo method. Moreover, a feedback mechanism is modeled to explain the elasticity performance of the proposed distributed system. The system extends the data locality to the edges in a trusted manner and ensures trust while scaling the whole system and increasing the number of nodes. By the help of such a trusted solution, lineage information of the data at the edges enable fault-recovery from an available checkpoint, while maximizing the trustworthiness of the overall system. Innovative distributed data structures, make databases fresh for all scaled nodes by unifying the memory resources; minimize the need to trusted third parties via trusted distributed data structures, which uses checksums of the datum periodically. Hence, multi-layer neural networks and hierarchical tree structures, has confidential data, can be updated and trained dynamically. Searching speed and performance of an object or set of objects in massive systems is maximized while keeping the trustworthiness of the total system. Initial results indicate that the trust cost worth to pay to scale and to keep the performance of the whole system. The System also shows good elasticity in the case of sudden provisioning/de-provisioning of control nodes. The proposed system also has satisfactory resource-allocation capability with efficient clustering thanks to the introduction of distributed ledger-based transaction management and lineage data recording for dynamic management of DAG structures, has sub-modular and disjoint cluster sets. Initial results of micro-blog analytics indicate promising performance of unified batch/interactive/ad-hoc querying with the holistic abstraction.
Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments, while supporting fault tolerance and persistence in real time, and provide real-time adaptation of learning algorithms to cope with new price and sentiment data. In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform is based on (i) a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way; (ii) an approach that supports sentiment analysis which can respond to large amounts of natural language processing queries in real time; and (iii) a predictive method grounded on online learning in which a model adapts its weights to cope with new prices and sentiments. Besides providing an architectural design, the paper also describes the KryptoOracle platform implementation and experimental evaluation. Overall, the proposed platform can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.
This study applies reinforcement learning (RL) from the AI machine learning field to derive an optimal Bitcoin-like blockchain mining strategy. A salient feature of the RL learning framework is that an optimal (or near-optimal) strategy can be obtained without knowing the details of the blockchain network model. Previously, the most profitable mining strategy was believed to be honest mining encoded in the default blockchain protocol. It was shown later that it is possible to gain more mining rewards by deviating from honest mining. In particular, the mining problem can be formulated as a Markov Decision Process (MDP) which can be solved to give the optimal mining strategy. However, solving the mining MDP requires knowing the values of various parameters that characterize the blockchain network model. In real blockchain networks, these parameter values are not easy to obtain and may change over time. This hinders the use of the MDP model-based solution. In this study, we employ RL to dynamically learn a mining strategy with performance approaching that of the optimal mining strategy. Since the mining MDP problem has a nonlinear objective function (rather than linear functions of standard MDP problems), we design a new multidimensional RL algorithm to solve the problem. Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multidimensional RL mining algorithm can still achieve optimal performance over time-varying blockchain networks.
Abstract A stable reference of Internet resources is crucial not only to identify a resource in a trustworthy and certified way but also to guarantee continuous access to it over time. The current practice in scientific publication as the use of a Persistent Identifier (PID) like a DOI or Handle, is becoming attractive also for the datasets. In fact, in the era of Big Data, the aspects of replicability and verification of the scientific result are paramount. In this paper we verify the functional feasibility of permissioned blockchain technology as a tool to implement a Trustworthy Persistent Identifier (T-PID) system for datasets in the scientific domain.
Francesco Zola, Jan L. Bruse, Maria Eguimendia, Mikel Galar · 5 authors
The Bitcoin network not only is vulnerable to cyber-attacks but currently represents the most frequently used cryptocurrency for concealing illicit activities. Typically, Bitcoin activity is monitored by decreasing anonymity of its entities using machine learning-based techniques, which consider the whole blockchain. This entails two issues: first, it increases the complexity of the analysis requiring higher efforts and, second, it may hide network micro-dynamics important for detecting short-term changes in entity behavioral patterns. The aim of this paper is to address both issues by performing a “temporal dissection” of the Bitcoin blockchain, i.e., dividing it into smaller temporal batches to achieve entity classification. The idea is that a machine learning model trained on a certain time-interval (batch) should achieve good classification performance when tested on another batch if entity behavioral patterns are similar. We apply cascading machine learning principles—a type of ensemble learning applying stacking techniques—introducing a “k-fold cross-testing” concept across batches of varying size. Results show that blockchain batch size used for entity classification could be reduced for certain classes (Exchange, Gambling, and eWallet) as classification rates did not vary significantly with batch size; suggesting that behavioral patterns did not change significantly over time. Mixer and Market class detection, however, can be negatively affected. A deeper analysis of Mining Pool behavior showed that models trained on recent data perform better than models trained on older data, suggesting that “typical” Mining Pool behavior may be represented better by recent data. This work provides a first step towards uncovering entity behavioral changes via temporal dissection of blockchain data.
Due to the limitation of the capacity of each block, when the number of transactions increases at the same time, it will face congestion problems and reduce the efficiency of accounting. Collaborative computing and data slicing are effective methods to improve performance, and they have been widely used in various fields. This paper proposes a tree chain architecture that adds peer-to-peer blockchain to main chain of the Ethereum. When there is a task to be processed in the main chain, the architecture assigns the task to "branch" or "leaf". The "branch" first determines whether the task can be sliced. If the task can be sliced, divide the task into leaf nodes for processing, otherwise let the branch nodes handle. By assigning tasks in this way, it is possible to help the main chain reduce the computational pressure and increase the system throughput.
Bitcoin-NG, a scalable blockchain protocol, divides each block into a key block and many micro blocks to effectively improve the transaction processing capacity. Bitcoin-NG has a special incentive mechanism (i.e. splitting transaction fees to the current and the next leader) to maintain its security. However, this design of the incentive mechanism ignores the joint effect of transaction fees, mint coins and mining duration lengths on the expected mining reward. In this paper, we identify the advanced mining attack that deliberately ignores micro blocks to enlarge the mining duration length to increase the likelihood of winning the mining race. We first show that an advanced mining attacker can maximize its expected reward by optimizing its mining duration length. We then formulate a game-theoretical model in which multiple mining players perform advanced mining to compete with each other. We analyze the Nash equilibrium for the mining game. Our analytical and simulation results indicate that all mining players in the mining game converge to having advanced mining at the equilibrium and have no incentives for deviating from the equilibrium; the transaction processing capability of the Bitcoin-NG network at the equilibrium is decreased by advanced mining. Therefore, we conclude that the Bitcoin-NG blockchain protocol is vulnerable to advanced mining attack. We discuss how to reduce the negative impact of advanced mining for Bitcoin-NG.
In blockchain ecosystems, an Oracle is a service tool which provides real-world data for smart contracts and other blockchain applications. At present, there are several Oracle implementation schemes, e.g. centralized Oracles, decentralized Oracles, and hardware Oracles. However, these schemes typically suffer from single source of data and low scalability. Application Specific Knowledge Engine (ASKE) is an integrated topic/application-centered knowledge portal that supports effective information retrieval and analysis. Inspired by ASKE, in this paper, we propose a novel Oracle implementation scheme. The proposed scheme can realize multi-source data extraction and analysis, then working prototypes are demonstrated to show the validity of the scheme.
C. G. Raji, A - Vinish, Ganesh Gopakumar, K Shahil
Cryptocurrency, a form of digital currency is the redistributed and open system that uses cryptography to reinforce security for the management of standard financial transactions. Administration of cryptocurrency mining demands significant hash functions which in turn necessitates a powerful rig. Here, we demonstrate the implementation of Cryptocurrency miner using Raspberry Pi system integrated with USB hash miners and with a CG-Miner software, installed in the operating system. Provision of a System on Single chips platform by Raspberry pi yields high compatibility, and the portable nature of the system assists the convoluted process of mining to achieve new horizons. The exponential rise of the cryptocurrency miners lead to the demand for an economical methodology for the mining process can be gratified by the Raspberry pi mining technique which help to quench the cost of the available systems providing a new cost effective and energy efficient technology.
In Bitcoin, if a miner is able to solve a computationally hard problem called proof of work, it will receive an amount of bitcoin as a reward which is the sum of the fees for the transactions included in a block plus an amount inversely proportional to the number of blocks discovered so far. At the moment of writing, the block reward is several orders of magnitude greater than the sum of transaction fees. Usually, miners try to collect the largest reward by including transactions associated with high fees. The main purpose of transaction fees is to prevent network spamming. However, they are also used to prioritize transactions. In order to use the minimum amount of fees, users usually have to find a compromise between fees and urgency of a transaction. In this paper, we develop a probabilistic logic model to experimentally analyze how fees affect confirmation time and miner’s revenue and to predict if an increase of average fees will generate a situation when the miner gets more reward by not following the protocol.
Thiago Luiz Gontijo de Almeida, Pierre François, Stéphane Frénot
The following topics are dealt with: storage management; cloud computing; decision making; peer-to-peer computing; distributed databases; cryptocurrencies; distributed processing; cryptography; data privacy; data mining.
Technology always bring opportunities to business process to evolve. This paper aims to approach blockchain technology using Business Process Model and Notation (BPMN). Hyperledger composer is one of the tools to make blockchain. Our study includes three Hyperledger composer concepts (assets, participants and transactions) and smart contract concept. This paper elaborates our past work on microservices in reservation-based parking system as our case study and focus in mapping BPMN to the three Hyperledger composer concepts mentioned above. Our finding includes identification of assets, participants and transactions with smart contract incorporation in our case study. The results also show BPMN pools and tasks changes. We present these changes in comparison table and revised BPMN diagram.
Ferdiansyah Ferdiansyah, Siti Hajar Othman, Raja Zahilah Raja Md Radzi, Deris Stiawan · 6 authors
Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market. Stock markets are influenced by many risks of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market. Because it's fluctuations, there's a need and automation tool to predict bitcoin on the stock market. This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency. The Method that we apply on this research, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.
Evan Brinckman, Andrey Kuehlkamp, Jarek Nabrzyski, Ian Taylor
As the public Ethereum network surpasses half a billion transactions and enterprise Blockchain systems becoming highly capable of meeting the demands of global deployments, production Blockchain applications are fast becoming commonplace across a diverse range of business and scientific verticals. In this paper, we reflect on work we have been conducting recently surrounding the ingestion, retrieval and analysis of Blockchain data. We describe the scaling and semantic challenges when extracting Blockchain data in a way that preserves the original metadata of each transaction by cross referencing the Smart Contract interface with the on-chain data. We then discuss a scientific use case in the area of Scientific workflows by describing how we can harvest data from tasks and dependencies in a generic way. We then discuss how crawled public blockchain data can be analyzed using two unsupervised machine learning algorithms, which are designed to identify outlier accounts or smart contracts in the system. We compare and contrast the two machine learning methods and cross correlate with public Websites to illustrate the effectiveness such approaches.
Machine learning and blockchain are two of the most notable technologies of recent years. The first is the foundation of artificial intelligence and big data analysis, and the second has significantly disrupted the financial industry. Both technologies are data‐driven, and thus there are rapidly growing interests in integrating both for more secure and efficient data sharing and analysis. In this article, we review existing research on combining machine learning and blockchain technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more research on deeper integration of these two promising technologies.
Hye-Yeong Shin, Daeyong Kim, Soohoon Maeng, Kiyoung Lee · 5 authors
Most of the existing platform for Bitcoin data analysis or visualization, they perform data collection from one node in the main-net. Thus we can notice some differences in their provided data. In this research, and in order to provide global data visualization, we collect both the historical and realtime Bitcoin data. Our monitoring system is comprised of three main components; multiple full Bitcoin nodes were used to collect data, the analysis of data was done by the Analytic Engine, while the webserver was used to visualize both row-data and analyzed data.
The online cryptocurrency bitcoin, created based in blockchain technology, is attracting the attention of individuals, businesses and the government as well. As interest in blockchain technology and cryptocurrency has steadily increased over the past few years, trading volume and market size of cryptocurrency have increased at an astonishing speed. As a result, analysis and monitoring measures for blockchain networks, blocks, and transactions have become an important issue. In this paper, the method of clustering applied dimension reduction as a method of bitcoin network analysis is proposed. The proposed method applies the analysis way using K-means algorithm with PCA to block data in bitcoin collected by this research team.
Data Stream Mining Techniques
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Cryptocurrency made on the basis of block-chain technology Bitcoin is drawing the attention of individuals, corporations, governments and financial institutions today. As the number of Bitcoin transactions increases over the past years, the scale of the Bitcoin market has been increasing day by day. Predicting the number of transactions contained in a Bitcoin block is important in a Bitcoin network. The aim of this paper is to propose a learning feature selection method for designing a machine learning model that predicts the number of transactions contained in the Bitcoin block by applying the machine learning algorithm. Selecting the appropriate feature to design a machine learning model is crucial things to the performance of the model. We apply correlation analysis to select the appropriate learning feature of the transaction count prediction model in the Bitcoin block and verify the validity of the proposed method through experiments.