K. Sri Lakshmi Sruthi, D. Ratnagiri, Rudru Jyothika, Salunkhe Sneha · 6 authors
Bitcoin is one of the most popular and valuable cryptocurrencies in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Bitcoin price prediction with different machine learning prediction algorithms. For the project: relevant features are taken from the dataset having strong correlation with Bitcoin prices and random data chunks are then selected to train and test the model. The random data which has been selected for model training, may cause unfitting outcomes thus reducing the price prediction accuracy. Here, a proper method to train a prediction model is being scrutinised. The proposed methodology is then applied to train a simple Long Short-Term Memory (LSTM) model to predict the bitcoin price for the upcoming 30 days. When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction. In the end of this project, the work culminates with future improvements. 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 project, 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.
Akshay M. Fajge, Subhasish Goswami, Arpit Srivastava, Raju Halder
The gas mechanism on the Ethereum blockchain attempts to set charges for every smart contract operation in order to prevent infinite control of computational resources by the executing transactions. To satisfy this requirement, users need to specify in their transactions how much gas fees (in terms of gas limit and gas price) they would like to pay for. In essence, these gas fees are paid to the miners in return for their computational services. Naturally, miners tend to maximize their profits by considering the transactions with higher gas fees, and as a result, the transactions with lower gas fees remain in the waiting pool for a long time. This paper proposes a machine learning-based approach to predict whether a transaction with offered gas fees is likely to be included in the blockchain within the expected time or not. Such prior prediction of transactions' waiting time definitely assists users to reset their gas fees accordingly. The proposed model is evaluated on nearly one million real transactions from Ethereum mainnet, and the experimental results demonstrate a better performance than the existing one in the literature, with an achievement of 90.18% accuracy and 0.897 F1-score when the model is trained with Random Forest on the dataset balanced with SMOTETomek.
Bitcoin is an innovative decentralized digital currency without intermediaries. Bitcoin price prediction is a demanding need in the present situation. This paper makes an investigation on the Bitcoin price forecast with a Bi-directional Gated Recurrent Unit (GRU) time series method, combined with opinion mining based on Twitter and Reddit feeds. An hourly basis sentimental analysis through the implementation of Natural Language Processing presents a positive impact of sentimental analysis on the Bitcoin price prediction. For prediction, RNN, long-short memory, GRU has been utilized. Unidirectional and Bi-directional versions of all three networks with and without sentimental analysis were implemented for comparison. Of all the techniques implemented Bi-directional GRU along with sentimental analysis gives a minimum RMSE and Minimum absolute percentage error of 1108.33 and 7.384%. Thus, the framework including Bi-Directional GRU along with Sentimental Analysis provides better results than the State-of-art methods.
Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.
Schahram Dustdar, Pablo Fernández, José María García, Antonio Ruiz–Cortés
In this paper, we deal with questions related to blockchains in complex Internet of Things (IoT)-based ecosystems. Such ecosystems are typically composed of IoT devices, edge devices, cloud computing software services, as well as people, who are decision makers in scenarios such as smart cities. Many decisions related to analytics can be based on data coming from IoT sensors, software services, and people. However, they are typically based on different levels of abstraction and granularity. This poses a number of challenges when multiple blockchains are used together with smart contracts. This work proposes to apply our concept of elasticity to smart contracts and thereby enabling analytics in and between multiple blockchains in the context of IoT. We propose a reference architecture for Elastic Smart Contracts and evaluate the approach in a smart city scenario, discussing the benefits in terms of performance and self-adaptability of our solution.
Schahram Dustdar, José María García, Pablo Fernández, Antonio Ruiz–Cortés
Smart contracts provide computation facilities to blockchains, enabling many application scenarios where automatic analysis and complex transactions can be performed. However, in situations where the flow of information is variable and there are time, quality, and/or cost constraints imposed, smart contracts do not usually offer enough performance. In this article, we describe an elasticity framework that enables dynamic adaptation of smart contracts with respect to used resources, analytics quality, and incurred costs in a transparent manner for both smart contract developers and users. We validate our framework in an Internet of Things and Smart City scenario where several analytics are computed using Elastic Smart Contracts that dynamically adapt the appropriate elasticity variables.
Digital currency aims to decentralize online transactions with high efficiency and increased security. These operations are carried out using blockchain based systems to eliminate the need for centralized verification and authorization. With the emergence of these systems come a vast number of cryptocurrencies such as bitcoins and ether that are widely used. The need for the analysis of the trend of digital currency arises because of the frequent fluctuation of their value in the markets. In this paper, the prediction of the trend of one such cryptocurrency, bitcoins, is performed using a Deep Belief Network model that is pre-trained using Restricted Boltzmann Machines for studying the data for 2019 with different time intervals: minute-by-minute, hour-by-hour and day-by-day. This would help to identify the trend of the cryptocurrency bitcoin for analyzing the demand supply dynamics of their market capital. The trained model was evaluated with the measures of MAE, MSE and compared with the existing state-of-the-art models. The minute-by-minute prediction model performed best with a RMSE of 25.87 and a MAE of 14.83.
Muhammad Saad, Victor Cook, Lan N. Nguyen, My T. Thai · 5 authors
Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted third party. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to the network dynamics. We verify our observations through data-driven analyses and simulations, and discuss the implications of each attack on the Bitcoin network. We conclude with suggested countermeasures.
Aug 7, 2021·In: Awan, I., Benbernou, S., Younas, M., Aleksy, M. (eds) The International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021). Deep-BDB 2021. Lecture Notes in Networks and Systems, vol 309. Springer, Cham
On the Ethereum network, it is challenging to determine a gas price that ensures a transaction will be included in a block within a user's required timeline without overpaying. One way of addressing this problem is through the use of gas price oracles that utilize historical block data to recommend gas prices. However, when transaction volumes increase rapidly, these oracles often underestimate or overestimate the price. In this paper, we demonstrate how Gaussian process models can predict the distribution of the minimum price in an upcoming block when transaction volumes are increasing. This is effective because these processes account for time correlations between blocks. We performed an empirical analysis using the Gaussian process model on historical block data and compared the performance with GasStation-Express and Geth gas price oracles. The results suggest that when transactions volumes fluctuate greatly, the Gaussian process model offers a better estimation. Further, we demonstrated that GasStation-Express and Geth can be improved upon by using a smaller training sample size which is properly pre-processed. Based on the results of empirical analysis, we recommended a gas price oracle made up of a hybrid model consisting of both the Gaussian process and GasStation-Express. This oracle provides efficiency, accuracy, and better cost.
Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.
Cryptocurrency market has witnessed a boom during the global pandemic and has proven as a strong investment with a wide institutional adoption. A time-series forecasting solution will play a vital role in analyzing the fluctuation of the bitcoin and altcoin markets. Dogecoin is one such altcoin that is a low-price, high-risk investment option garnering considerable interest this year. The variation of the price trend of this altcoin is studied using the multi-head attention mechanism implemented in a transformer, where the attention heads attend to the tokens that are relevant to each current token based on varying short-term and long-term dependencies. In this paper, a multi-head attention-based transformer encoder-decoder model is applied on the hourly data of the Dogecoin price for its prediction over time. The performance of the model has been evaluated using a number of evaluation metrics including MAE and predictive R-squared value. The model trained over the Dogecoin hourly price variation gave an impressive accuracy of 98.46% and R-squared value of 0.8616 comparable with the existing state-of-the-art cryptocurrency price forecasting models.
Rawya Mars, Amal Abid, Saoussen Cheikhrouhou, Slim Kallel
Ethereum is a blockchain-based platform that pro-vides a global computational infrastructure to run smart contracts. In order to assign a cost to smart contract and transaction execution, the Ethereum Blockchain adopts a gas-based metering approach which is designed to motivate miners to operate the network and protect it against attacks. More precisely, miners receive fees from all transactions included in the mined block in addition to the mining reward. Hence, the higher the gas price in the transactions, the higher the fee paid to the miner will be, resulting in faster selection and execution of higher priced gas transactions. Therefore, an Ethereum transaction sender is exposed to the non-trivial task of having to choose an optimal gas price, as underpaying likely results in a transaction not being picked by miners, whereas overpaying leads to superfluous costs. This paper provides recommendation approach that proposes an appropriate gas price to users. More precisely, it investigates different approaches of forecasting algorithms applied for gas price predictions for the next block in Ethereum Blockchain. The gas price is predicted using the Prophet model and the deep learning models, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). It also aims to compare these approaches with the most used gas price oracles. An evaluation of the obtained results show that the LSTM and GRU proposed models outperform Prophet model as well as the gas price oracle Geth. In this case, LSTM and GRU provide a low mean squared error (MSE) of 0,008 whereas Geth gives an MSE of 0.016 and Prophet gives an MSE of 0.014.
This article analyzes the effectiveness of deploying the Blockchain technology in the implementation of the IoT ecosystem database. To this end, we assess the processing efficiency of transactions originated by smart devices and the storeddata integrity. The processing-efficiency evaluation is carried out through queue-theory-based analytical modeling, in which the average time for transaction confirmation is estimated. By its turn, the data-integrity is measured through simulations, where the probability of fraudsters altering already-stored data is estimated. Moreover, the experiments consider a set of scenarios related to different application domains. Final results show that the Blockchain technology may meet IoT efficiency requirements, besides providing adequate data integrity. Lastly, general conclusions and avenues for further research close this article.
Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.
Despite the fact that it is publicly available, collecting and processing the full bitcoin blockchain data is not trivial. Its mere size, history, and other features indeed raise quite specific challenges, that we address in this paper. The strengths of our approach are the following: it relies on very basic and standard tools, which makes the procedure reliable and easily reproducible; it is a purely lossless procedure ensuring that we catch and preserve all existing data; it provides additional indexing that makes it easy to further process the whole data and select appropriate subsets of it. We present our procedure in details and illustrate its added value on large-scale use cases, like address clustering. We provide an implementation online, as well as the obtained dataset.
Vikas Goel, Narendra Kumar, Amit Kumar Gupta, Sachin Kumar
In recent times, the two technologies, artificial intelligence (AI) and blockchain, are the most trending and disruptive in all technologies. Undoubtedly both technologies are covering all the areas of engineering and science with an exceptional rate. These technologies offer various degrees of technological complexity and multi-dimensional business implications. Blockchain is an open, distributed ledger technology that records all the transactions done. These distributed ledger technologies have disrupted the evolving technology sector. Blockchain can authorize and automate payment transactions in cryptocurrency. Also, blockchain has provided access to a shared ledger (block) of data, digital transactions, and logs in an environment that is decentralized, secure, and trusted. Blockchain governs the interactions among distributed participants with the capability of no intermediary or a trusted third party. Blockchain technology is evolving day by day with a fast-passed pace. Now, it is possible to integrate blockchain with many other evolving technologies like robotics with AI services. AI provides intelligence and decision-making capabilities like humans to machines. Data is the main constituent to develop AI and machine learning. Now, AI has a wide variety of uses from finance (stock trading) to web (chatbots) to advanced technology (self-driving cars). Before the advent of big data, AI was a conceptual model. Artificial intelligence (AI) works significantly as a strong analytic tool to analyze big data in a scalable, accurate, and real-time. By using artificial intelligence, the design and development of tools for big data analysis has certain issues like centralized architecture, security, privacy, resource constraints, and lack of enough training data. There is rarely today any human activity, machine control, and business logic that is not considered artificial intelligence in future years and decades. On the contrary, blockchain considers decentralized architecture as an emerging technology. Among the various distributed nodes of the IoT network, secure sharing of data and resources is provided by the blockchain. It is expected to remove centralized control and may overcome the other existing challenges in AI. In this chapter, we consider, emphasize, and analyze how blockchain implementation is supported or enhanced via various AI techniques. We will further review how artificial intelligence may be exploited to achieve the goal of blockchain 2.0. There seem to be numerous possibilities in the alliance of AI and blockchain in Industry 4.0.
Collaborations among multiple organizations, such as financial institutions, medical centers, and retail markets in decentralized settings are crucial to providing improved service and performance. However, the underlying organizations may have little interest in sharing their local data, models, and objective functions. These requirements have created new challenges for multi-organization collaboration. In this work, we propose Gradient Assisted Learning (GAL), a new method for multiple organizations to assist each other in supervised learning tasks without sharing local data, models, and objective functions. In this framework, all participants collaboratively optimize the aggregate of local loss functions, and each participant autonomously builds its own model by iteratively fitting the gradients of the overarching objective function. We also provide asymptotic convergence analysis and practical case studies of GAL. Experimental studies demonstrate that GAL can achieve performance close to centralized learning when all data, models, and objective functions are fully disclosed.
Joshi Padma Narasimhachari, B Sharon Angel, B. Shwetha Bindu
The goal of this paper is to determine how well the Bitcoin volume per USD can be predicted. The Bitcoin Price Index contains price information. The work is accomplished to varying degrees of success by employing the Bayesian optimised recurrent neural network (RNN) and the Long Short Term Memory (LSTM) network. LSTM achieves a maximum accuracy of 52% and an RMSE of 8%. The popular ARIMA model for time series is used to compare with in-depth learning models. In-depth offline learning methods outperform ARIMA's poor performance forecast, as expected. Finally, both in-depth learning models are marked on both GPU and CPU, with GPU training time improving CPU implementation by 67.7%. Bitcoin, Deep Learning, GPU, Recurrent Neural Network, Long-Term Memory, ARIMA are index terms.
G. M. Siddesh, S. R. Mani Sekhar, S R Vighnesh, N. Jagadeesh Sai · 6 authors
Supply chain management is the broad range of activities required to plan, control, and execute the flow of a product. As a less corruptible and more automated alternative to traditional databases, blockchains are well suited to the complicated record-keeping. However distributed database management system is a centralized software system; the blockchain technology can overcome the problem of synchronization between multiple databases; it also ensures that integrity problems are solved. In the proposed model, Ethereum blockchain is used to solve a few major supply chain problems to manage a distributed database. The model has incorporated techniques to predict the rise and fall of the demand for the medicine in the market by using machine learning algorithms such as linear regression and LSTM; also, the trend predicted by both the models has been compared. The result shows that while using linear regression the predicted trend is not very accurate and cannot trace the actual trend closely whereas BLSTM has performed well in predicting the trends of time series data.
The Bitcoin blockchain is managed by an under- lying peer-to-peer network. This network is responsible for the propagation of transactions carried out by users via the blocks (which contain the validated transactions), and to ensure consensus between the different nodes. The quality and safety of this network are therefore particularly essential. In this work, we present an open dataset on the peers composing the Bitcoin P2P Network that was made following a well defined and reproducible methodology. We also provide a first analysis of the dataset on three criteria: the number of public nodes and their client version and geographical distribution.
Jan A. Fischer, Andres Palechor, Daniele Dell’Aglio, Abraham Bernstein · 5 authors
Bitcoin is built on a blockchain, an immutable decentralised ledger that\nallows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins\nare associated with alpha-numeric addresses and are transferred via\ntransactions. Each transaction is composed of a set of input addresses\n(associated with unspent outputs received from previous transactions) and a set\nof output addresses (to which Bitcoins are transferred). Despite Bitcoin was\ndesigned with anonymity in mind, different heuristic approaches exist to detect\nwhich addresses in a specific transaction belong to the same entity. By\napplying these heuristics, we build an Address Correspondence Network: in this\nrepresentation, addresses are nodes are connected with edges if at least one\nheuristic detects them as belonging to the same entity. %addresses are nodes\nand edges are drawn between addresses detected as belonging to the same entity\nby at least one heuristic. %nodes represent addresses and edges model the\nlikelihood that two nodes belong to the same entity %In this network, connected\ncomponents represent sets of addresses controlled by the same entity. In this\npaper, we analyse for the first time the Address Correspondence Network and\nshow it is characterised by a complex topology, signalled by a broad, skewed\ndegree distribution and a power-law component size distribution. Using a\nlarge-scale dataset of addresses for which the controlling entities are known,\nwe show that a combination of external data coupled with standard community\ndetection algorithms can reliably identify entities. The complex nature of the\nAddress Correspondence Network reveals that usage patterns of individual\nentities create statistical regularities; and that these regularities can be\nleveraged to more accurately identify entities and gain a deeper understanding\nof the Bitcoin economy as a whole.\n
The challenge of collecting large scale environmental data lies in reducing the cost of allocating data sensors while improving the data quality. Compared with traditional centralized data collecting methods, obtaining environmental data by distributed “crowds” can largely reduce the cost. However, designing such crowdsourcing systems requires an appropriate incentive mechanism to encourage providing accurate and rare environmental data. In this paper, we propose to utilize blockchain-based incentive mechanisms to address the problem. We design and evaluate a blockchain-based system for large scale environmental data acquisition, which consists of a sensor layer to collect distributed environmental data, a valuation layer to evaluate the quality of the data collected, a consensus layer to incentive and motivate high quality data collection, and a ledger layer to record the incentive transactions and the qualified environmental data. The incentive mechanism in the consensus layer is achieved with a Proof-of-Data-Value (PODV) protocol adapted from the Practical Byzantine Fault Tolerance (PBFT) [11] consensus algorithm. We carry out experiments to compare the PODV with contemporary blockchain census protocols including Proof-of-Work (POW) [16] and Proof-of-Stake (POS)[18]. The experimental results show that the proposed system outperforms in encouraging crowds to provide accurate and rare environmental data, and imply that the PODV are superior in terms of throughput and environmental friendliness.