Blockchain is a decentralized database, which can protect the safety of trade and avoid double payment. Due to the widespread attention of researchers, the studies of this field have increased sharply in recent years. It is meaningful to reveal the development level and trends based on this literature. This paper adopts bibliometric methods to study the collaboration characteristics from the levels of author, institution and country. Furthermore, several kinds of collaboration networks and their centrality analysis are also presented, which not only display the development level and collaboration degree but also the evolution of author collaboration modes in different phases.
A Chatbot is a popular platform to enable users to interact with a software or website to gather information or execute actions in an automated fashion. In recent years, chatbots are being used for executing financial transactions, however, there are a number of security issues, such as secure authentication, data integrity, system availability and transparency, that must be carefully handled for their wide-scale adoption. Recently, the blockchain technology, with a number of security advantages, has emerged as one of the foundational technologies with the potential to disrupt a number of application domains, particularly in the financial sector. In this paper, we forward the idea of integrating a chatbot with blockchain technology in the view to improve the security issues in financial chatbots. More specifically, we present BONIK, a blockchain empowered chatbot for financial transactions, and discuss its architecture and design choices. Furthermore, we explore the developed Proof-of-Concept (PoC), evaluate its performance, analyse how different security and privacy issues are mitigated using BONIK.
Abstract In this work, the issue of predicting the edge weight in Bitcoin network has been addressed by leveraging community structure that involves members who trust with each other in their transactions. The proposed model consists of two main stages; the first one is the detection of trusted Bitcoin communities by implementing Newman- Girvan algorithm. In the context, the attributes of node have been modeling in different ways to get different structure of communities each time. Secondly, prediction the missing edge weight based on the neighbors of edge-source in community. In other words, the trust values that pointed to edge-target by neighbors are averaged to represent the prediction of missing edge weight. Practically, the model has been evaluated using two real-world datasets; Bitcoin-OTC and Bitcoin-Alpha datasets. The experimental results explicate the effectiveness of the proposed model comparable with other methods, where the minimization percentage for Bitcoin-OTC dataset is 4% and 18% for all and partial edges respectively. As for Bitcoin-Alpha dataset are 0% and 30% for all and partial edges respectively.
Flash Loan, as an emerging service in the decentralized finance ecosystem, allows users to request a non-collateral loan. While providing convenience, it also enables attackers to launch malicious operations with a large amount of asset that they do not have. Though there exist spot media reports of attacks that leverage Flash Loan, there lacks a comprehensive understanding of existing Flash Loan services. In this work, we take the first step to study the Flash Loan service provided by three popular platforms. Specifically, we first illustrate the interactions between Flash Loan providers and users. Then, we design three patterns to identify Flash Loan transactions. Based on the patterns, 76, 303 transactions are determined. The evaluation results show that the Flash Loan services get more popular over time. At last, we present four Flash Loan applications with real-world examples and propose two potential research directions.
In recent years, bitcoin has become a very attractive investment in financial industry, which is not controlled by governments, but is based on trust between transfers under the technology of block chain. Hence, forecasting future bitcoin cryptocurrency values is a problem that has attracted the attention of many researchers in the field, while proving to be a very challenging problem. This work presents an experimental analysis using LSTM and GRUs for forecasting bitcoin values in a minute-granulated time for the entire next day. To this end we also present our methodology for conducting the experiments. The final goal is to create the core of a financial prediction tool around the RNNs. In our experiments, we achieved interesting results such as a SMAPE of 0.0002, a RMSE of US$ 3.844 and a rRMSE of 0.0028 in a day where bitcoin rates vary from US$ 13.2K and US$ 14.6K, surpassing the results of SMAPE found in the literature and proposed limit of SMAPE smaller than 0.007 for forecasts.
Yue Wu, Liangtu Song, Lei Liu, Jincheng Li · 6 authors
Applying blockchain technology to the Internet of Things (IoT) remains a huge challenge. To meet the actual needs of IoT, a lightweight and high-throughput consensus mechanism, combined with blockchain technology, is proposed in this study. Blockchain nodes use the Diffie–Hellman algorithm for key negotiation. Sensors and blockchain nodes can use the shared key to generate HMAC (Hash-based Message Authentication Code) signatures for sensor-aware transactions and use the Verifiable Random Function to implement block nodes. Offline fast election, which is the node that wins the election, becomes the block node. Machine learning methods are also introduced to identify or remove outliers in the sensor data before such data are uploaded to the chain. Experimental results show that the system throughput synchronously increases as the test load increases. Moreover, when the test load is 800 tps, the system throughput reaches the maximum, close to 600 tps. When the test load exceeds 800 tps, the actual system throughput starts to drop, and approximately 90% of transactions have a delay time within 5000 ms. This method can be used in a lightweight IoT system.
Abstract The paper deals with the architecture and design of the complex distributed classifier for the intelligent video surveillance systems considering the contemporary tendency to detect the abnormal or suspicious behavior of the individuals by means of behavioral features set analysis. This paper focuses on the implementation of multiagent systems concept and the distributed ledger technology to the distributed message board architecture. Two selected approaches to the distributed ledger implementation are analyzed and estimated in terms of classifiers cooperation. Some simulation results are provided and discussed in terms of time consumption.
This paper studies how a simple approach based on fuzzing testing can help authors of Solidity contracts to accurately estimate the gas cost of services specified in a contract. Our fuzzer creates a private blockchain and randomly generates transactions. Such an environment is meant to simulate large scale behavior that may be seen in a public blockchain. Our fuzzer handles Ethereum starting and target endpoints in a transaction to accommodate requirements expressed in financial contracts. By comparing the gas computation made by the Ethereum Solidity compiler and the actual consumption during our fuzzing, we are able to find discrepancies between predicted and real gas consumption. Our findings are beneficial to transaction authors to correctly predict the computing resources of Ethereum miners.
Bitcoin, as one of the most popular cryptocurrency, is recently attracting\nmuch attention of investors. Bitcoin price prediction task is consequently a\nrising academic topic for providing valuable insights and suggestions. Existing\nbitcoin prediction works mostly base on trivial feature engineering, that\nmanually designs features or factors from multiple areas, including Bticoin\nBlockchain information, finance and social media sentiments. The feature\nengineering not only requires much human effort, but the effectiveness of the\nintuitively designed features can not be guaranteed. In this paper, we aim to\nmining the abundant patterns encoded in bitcoin transactions, and propose\nk-order transaction graph to reveal patterns under different scope. We propose\nthe transaction graph based feature to automatically encode the patterns. A\nnovel prediction method is proposed to accept the features and make price\nprediction, which can take advantage from particular patterns from different\nhistory period. The results of comparison experiments demonstrate that the\nproposed method outperforms the most recent state-of-art methods.\n
Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, anomaly detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate anomaly detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and anomaly detection in the GPU. We also propose a conditional feature extraction method to reduce computation cost of anomaly detection. We employ anomaly detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 8.6 times faster than CPU processing method and 2.6 times faster than GPU processing method that does not perform feature extraction on the GPU. In addition, the conditional feature extraction method achieves 1.7 times faster than the unconditional method when the number of users satisfying a given condition is 200 thousands out of one million.
This paper describes the Distributed Ledger Network Analyzer (DiLeNA), a new software tool for the analysis of the transactions network recorded in Distributed Ledger Technologies (DLTs). The set of transactions in a DLT forms a complex network. Studying its characteristics and peculiarities is of paramount importance, in order to understand how users interact in the distributed ledger system. The tool design and implementation is introduced and some results are provided. In particular, the Bitcoin and Ethereum blockchains, i.e. the most famous and used DLTs at the time of writing, have been analyzed and compared.
Bitcoin and its decentralized computing paradigm for digital currency trading are one of the most disruptive technology in the 21st century. This article presents a novel approach to developing a Bitcoin transaction forecast model, DLForecast, by leveraging deep neural networks for learning Bitcoin transaction network representations. DLForecast makes three original contributions. First, we explore three interesting properties between Bitcoin transaction accounts: topological connectivity pattern of Bitcoin accounts, transaction amount pattern, and transaction dynamics. Second, we construct a time-decaying reachability graph and a time-decaying transaction pattern graph, aiming at capturing different types of spatial-temporal Bitcoin transaction patterns. Third, we employ node embedding on both graphs and develop a Bitcoin transaction forecasting system between user accounts based on historical transactions with built-in time-decaying factor. To maintain an effective transaction forecasting performance, we leverage the multiplicative model update (MMU) ensemble to combine prediction models built on different transaction features extracted from each corresponding Bitcoin transaction graph. Evaluated on real-world Bitcoin transaction data, we show that our spatial-temporal forecasting model is efficient with fast runtime and effective with forecasting accuracy over 60 percent and improves the prediction performance by 50 percent when compared to forecasting model built on the static graph baseline.
A proof of work (PoW) blockchain protocol distributes rewards to its participants, called miners, according to their share of the total computational power. Sufficiently large miners can perform selfish mining - deviate from the protocol to gain more than their fair share. Such systems are thus secure if all miners are smaller than a threshold size so their best response is following the protocol. To find the threshold, one has to identify the optimal strategy for miners of different sizes, i.e., solve a Markov Decision Process (MDP). However, because of the PoW difficulty adjustment mechanism, the miners' utility is a non-linear ratio function. We therefore call this an Average Reward Ratio (ARR) MDP. Sapirshtein et al.\ were the first to solve ARR MDPs by solving a series of standard MDPs that converge to the ARR MDP solution. In this work, we present a novel technique for solving an ARR MDP by solving a single standard MDP. The crux of our approach is to augment the MDP such that it terminates randomly, within an expected number of rounds. We call this Probabilistic Termination Optimization (PTO), and the technique applies to any MDP whose utility is a ratio function. We bound the approximation error of PTO - it is inversely proportional to the expected number of rounds before termination, a parameter that we control. Empirically, PTO's complexity is an order of magnitude lower than the state of the art. PTO can be easily applied to different blockchains. We use it to tighten the bound on the threshold for selfish mining in Ethereum.
Ilham Qasse, Josef Spillner, Manar Abu Talib, Qassim Nasir
Repositories are important indicators for liveness and maturity in software development communities. They host user-facing applications or re-usable artefacts to build such applications. While rarely decentralised themselves, they are important for hosting code for decentralized applications. In this study, we investigate public repositories dedicated to decentralised applications, or ĐApps, executing on heterogeneous blockchain platforms. The study is the first to report aggregated metrics on the repository-level and application-level characteristics including ĐApps metadata, associated smart contracts composition and inconsistencies between repositories in both schema and content. The main contributions are data acquisition tools and an evolving public dataset along with an initial analysis to derive key metrics in a reproducible way. Insights provided encompass the dominance of Ethereum, the absence of smart contracts for a significant portion of applications, and unused application advertisement potential by absence from popular repositories. The insights can be exploited by developers to build high-quality and highly popular applications and set up corresponding quality checks.
Iván García‐Magariño, Moustafa M. Nasralla, Shah Nazir
Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all these phases by (a) monitoring online information from websites with Selenium-based software and incrementally conforming a database, and (b) incrementally updating summarized information to support real-time decisions. We have illustrated this approach for the investor–company field with the particular fields of Bitcoin cryptocurrency and Internet-of-Things (IoT) smart-meter sensors in smart cities. The results of 40 simulations on historic data showed that one of the proposed investor strategies achieved 7.96% of profits on average in less than two weeks. However, these simulations and other simulations of up to 69 days showed that the benefits were highly variable in these two sets of simulations (respective standard deviations were 24.6% and 19.2%).
Yu‐Pin Lin, Hussnain Mukhtar, Kuan-Ting Huang, Joy R. Petway · 7 authors
Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a “backward pollution source tracing” (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.
Reshma Sundari Gadey, Nikita Thakur, Naveen Charan, R. Obulakonda Reddy
With the development Machine learning and AI-assisted trading has gained interest in the past few years. To bring out the abnormal profits from the cryptocurrency market, we use this machine learning and AIassisted trading. We store the daily data for a certain period. With the strategies assisted by state-of-the-art algorithms we obtain great outcomes. With the help of simple algorithms and architecture, the outcomes made the growth in the cryptocurrency market. The cryptocurrency has become popular in 2017 because of the growth in market capitalization. More than 1500 crypto currencies are actively trading in today's scenario. The crypto currency can be generated and be used for online transactions. Bitcoin is a cryptocurrency technology. The value of Bitcoin keeps varying every second. Therefore, to predict the value of bitcoin price here, we use the LSTM Architecture. With the help of this architecture, we are trying to prove this LSTM architecture provides more accurate results than any other machine learning algorithms and architecture.
Hyperparameters are pivotal for machine learning models. The success of efficient calibration, often surpasses the results obtained by devising new approaches. Traditionally, human intervention is required to tune the models, however, this obtuse outlook restricts the proficiency and competence. Automating this crucial characteristic of learning sustainably, proffers a significant boost in performance and cost optimization. Blockchain technology has revolutionized industries utilizing its Proof-of-Work algorithms for consensus. This complicated solution generates a lot of useless computations across the nodes attached to the network and thus, fritters away a huge amount of precious energy. In this paper, we propose to exploit these inane computations for training deep learning models instead of calculating purposeless hash values, thus, suggesting a new consensus schema. This work distinguishes itself from other related works by capitalizing on the parallel processing prospects it generates for hyperparameter tuning of complex deep learning models. We address this aspect through the framework of Bayesian optimization which is an effective methodology for the global optimization of functions with expensive evaluations. We call our work, Proof of Deep Learning with Hyperparameter Optimization (PoDLwHO).
Blockchain is a public distributed ledger, which has the characteristics of decentralization and anonymization, which leads to the frequent occurrence of money laundering and theft. Taking Bitcoin as an example, traders can have multiple addresses, and these addresses have nothing to do with their identities in real life, their identities are difficult to identify, and it is difficult to track the flow of transaction funds on the blockchain. This paper proposes a transaction tracking system that can effectively and accurately track the source and destination of a certain amount of funds on the blockchain, which is superior to existing Bitcoin transaction tracking methods and has a substantial reference value.
Petrônio Cândido de Lima e Silva, Carlos Alberto Severiano, Marcos Antônio Alves, Rodrigo Silva · 6 authors
In this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS) method with time varying parameters adapted from the distribution of the data. In this approach, we employ Non-Stationary Fuzzy Sets, in which perturbation functions are used to adapt the membership function parameters in the knowledge base in response to statistical changes in the time series. The proposed method is capable of dynamically adapting its fuzzy sets to reflect the changes in the stochastic process based on the residual errors, without the need to retraining the model. This method can handle non-stationary and heteroskedastic data as well as scenarios with concept-drift. The proposed approach allows the model to be trained only once and remain useful long after while keeping reasonable accuracy. The flexibility of the method by means of computational experiments was tested with eight synthetic non-stationary time series data with several kinds of concept drifts, four real market indices (Dow Jones, NASDAQ, SP500 and TAIEX), three real FOREX pairs (EUR-USD, EUR-GBP, GBP-USD), and two real cryptocoins exchange rates (Bitcoin-USD and Ethereum-USD). As competitor models the Time Variant fuzzy time series and the Incremental Ensemble were used, these are two of the major approaches for handling non-stationary data sets. Non-parametric tests are employed to check the significance of the results. The proposed method shows resilience to concept drift, by adapting parameters of the model, while preserving the symbolic structure of the knowledge base.
A blockchain, such as Bitcoin, is an append-only, secure, transparent, distributed ledger. A fair blockchain is expected to have healthy metrics; high honest mining power, low processing latency, i.e., low wait times for transactions and stable price of consumption, i.e., the minimum transaction fee required to have a transaction processed. As Bitcoin matures, the influx of transactions increases and the block rewards become insignificant. We show that under these conditions, it becomes hard to maintain the health of the blockchain. In Bitcoin, under these mature operating conditions (MOC), the miners would find it challenging to cover their mining costs as there would be no more revenue from merely mining a block. It may cause miners not to continue mining, threatening the blockchain's security. Further, as we show in this paper using simulations, the cost of acting in favor of the health of the blockchain, under MOC, is very high in Bitcoin, causing all miners to process transactions greedily. It leads to stranded transactions, i.e., transactions offering low transaction fees, experiencing unreasonably high processing latency. To make matters worse, a compounding effect of these stranded transactions is the rising price of consumption. Such phenomena not only induce unfairness as experienced by the miners and the users but also deteriorate the health of the blockchain. We propose BitcoinF transaction processing protocol, a simple, yet highly effective modification to the existing Bitcoin protocol to fix these issues of unfairness. BitcoinF resolves these issues of unfairness while preserving the ability of the users to express urgency and have their transactions prioritized.
Currently, there are hundreds of Bitcoin exchanges on the market, so choosing a reliable exchange is a critical issue for users. We know that the amount of Bitcoin holdings is an essential indicator for evaluating an exchange, but people have very few ways to access this information. Besides, many reports indicate that the trading volumes of most Bitcoin exchanges do not match their real situations, and the fake volume has become an unspoken rule of the whole industry. It causes the public to doubt the actual amount of Bitcoin owned by each exchange. To solve the problem of information asymmetry between users and exchanges, we propose a method for tagging Bitcoin addresses of exchanges. Through vertical, forward, and backward address mining, the method can utilize only one or several addresses of an exchange to find out all its addresses and distinguish different address types: deposit wallet, hot wallet, and cold wallet. Then the balance and transfers of the exchange can be further obtained through these addresses, helping users understand the real Bitcoin holdings of the exchange. Several experiments are conducted to evaluate the effectiveness of the proposed Bitcoin address tagging method. Our method has very little dependence on off-chain information. Only one address is needed for each exchange as a seed to find out all the other addresses. Such a seed address can be easily obtained by depositing some Bitcoin into the exchange or withdrawing some from it, which makes our method feasible for all exchanges.
To make good use of valuable Internet of Things (IoT) data assets, this paper proposes a trust-aware IoT data economic system (TIDES) with complete IoT data pricing, trading and protection functions. To ensure reliable and automatic data trading, the entire trading process is automatically performed by smart contracts on a hierarchical blockchain. Moreover, we develop several sophisticated methods to ensure the efficiency and service quality of TIDES. First, a complete evaluation model that takes the data trading profile and reputation into consideration is proposed for both suppliers and demanders to assess the trustworthiness of their trading partners. Second, a client-centric data value evaluation model and a game-theory-based pricing model are used to promote win-win transactions in which the demanders obtain higher quality data at an acceptable price and the suppliers receive higher profits. Third, a dispute arbitration model is invoked to detect suspicious trading and refund these payments automatically. TIDES further utilizes a multi-access edge computing (MEC) architecture to alleviate the huge burdens of IoT devices from blockchain operations, reduce the trading latency, and help mobile devices to trade IoT data. The simulation results have shown the advantages of TIDES in terms of trading time, storage overhead, data trading profit, quality data trading, pricing efficiency, and reliability on data asset management and trading.
Bitcoin is a virtual and decentralized cryptocurrency that operates in a peer-to-peer network providing a private payment mechanism. It is a multi-billion dollar cryptocurrency, and hundreds of other cryptocurrencies are created based on it. Bitcoin is based on Open-Source (OSS) software development, and OSS is a convenient way to qualitatively measure software development and growth. This thesis presents the first comprehensive study of the Bitcoin ecosystem in GitHub organized around 481 most popular and actively developed Bitcoin related projects over eight years (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018).