P. M. Yawalkar, Deepak Narayan Paithankar, Abhijeet Rajendra Pabale, Rushikesh Vilas Kolhe · 5 authors
An integrated identity is a centralized identifier that makes it possible for customers to get access to a variety of business services from a single network. The risks and assaults include identity leaks, centralized management, auditing restrictions, and lengthy breach investigation procedures. The article presents a technique for creating a blockchain-based, integrated identification system in a marketplace by automating and decentralizing the creation and auditing of strong and secure attributes. When individuals engage in market transactions, they act as nodes in a distributed blockchain network, contributing to the development of federated identities. Using a single federated identity, members of this network are able to use any of the participating companies' services. In this, IoT sensors and wearables can automatically log real-time data for you while identifying patterns and flagging problems. The total transparency of all blockchain transactions provides participants the ability to see which services they're using and their users the ability to track the usage of their identities. To test the proposed architecture, implementation done on a public blockchain and a permissioned blockchain (Ethereum and Hyperledger Fabric).
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.
Contemporarily, the popularity and use of cryptocurrencies has risen along with their prices and Ethereum is the second most popular and largest cryptocurrency after Bitcoin. Cryptocurrencies are based on the blockchain, which is a decentralized technology that has the power to change any banking system. They have become an attractive investment for both traders and individuals looking to invest. The price of Ethereum fluctuates and is affected by various factors, e.g., the crypto trading exchange as well as supply and demand. Ethereum is so valuable because it can be used as cash and one also pay Ethereum in full or in part to someone in exchange. Besides, it is easily guaranteed by the blockchain. Unlike stocks, the price of Ethereum is much more variable because it is traded 24 hours a day and there are no closing times. On this basis, this paper compares the results of two different models, namely linear regression and Long Short-Term Memory networks (LSTM). The dataset comprised in the closing prices of the last 372 days for Ethereum. The performance of the obtained models is critically evaluated using statistical indicators Root Mean Squared Error (RMSE) and the study have drawn our conclusions based on the RMSE result. The paper demonstrates a technique for using time series data in both models and determining each model's RMSE. These results shed light on guiding further exploration of prediction Ethereum prices and trends.
Contemporarily, cryptocurrency has a high market value, and the price of cryptocurrency fluctuates dramatically. This article analyzes the parameters effects of the LSTM model on Bitcoin price prediction accuracy based on Python and modules of Numpy, Pandas, Keras, Tensorflow, and Sklern. The analysis clarifies the relationship between the accuracy of Bitcoin price prediction and different parameters in the LSTM model. It is discovered that when larger batch sizes are supplied at minor epochs, the accuracy of Bitcoin price prediction declines. Meanwhile, the number of neurons affects the accuracy. In addition, compared to lengths of 14, 30, and 60, the prediction error grows greater when a single time sequence is 7 in length. Apart from that, at present, using closing prices from the past two years rather than the past 1 year, 3 years, or 5 years can make predictions more accurate. These findings shed light on recommendations for adjusting various parameters in the development of the LSTM model for Bitcoin price prediction.
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.
As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research.
Indranil Ghosh, Esteban Alfaro, Matías Gámez, Noelia García
Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.
Minh Hoang Nguyen, Phuong Duy Huynh, Son Hoang Dau, Xiaodong Li
The rapid development of blockchain and cryptocurrency in the past decade has created a huge demand for digital trading platforms. Popular decentralised exchanges (DEXs) such as Uniswap and PancakeSwap were created to address this market gap, facilitating cryptocurrency exchange without intermediaries and hence eliminating security and privacy issues associated with traditional centralised platforms. This, however, due to lack of regulation, results in the emergence of a host of damaging investment fraudulent schemes, including Ponzi, honey pot, pump-and-dump, and rug-pull.In this study, we aim to investigate the problem of detecting rug-pull on Uniswap using supervised learning. We aggregate a list of 23 features and propose the use of a hybrid feature selection technique to find the most relevant features for rug-pull. The classifier, using this refined set of features, outperforms the classifier in the previous studies and achieves an f1-score of 99%, a precision of 97% on non-malicious tokens, and a recall of 99% on malicious tokens. Additionally, we show that the XGBoost classifier, built using these proposed features, can distinguish scam tokens and newly listed tokens, which are often harder to differentiate as they have similar characteristics, and also propose a validation method.
As a new type of digital currency, Bitcoin is considered as “future gold” by various scholars. Therefore, this study considers Bitcoin and gold as a group of hedging assets to conduct investment research and it also discusses the investment rules between Bitcoin and gold: prediction of the rise and fall of Bitcoin, comparison of the characteristics of Bitcoin and gold, and the impact of the transaction procedures of Bitcoin and gold on the final trading results, and formulates trading strategies through optimization algorithms. Then, four machine learning algorithms, i.e., LSTM, BP neural network, Adaboost, and Bagging, are introduced to predict the rise and fall of gold and Bitcoin the next day, and then, the entropy weight method is used to synthesize four predicted results to ensure the robustness of the predicted results. To establish the optimal trading strategy, this study considers the maximum expected return as the goal to develop a single-objective optimization model and historical five-day price volatility as a risk factor. In this study, ant colony, simulated annealing, and genetic algorithms are used to solve the single-objective optimization model. Finally, we conclude that Bitcoin, similar to other financial assets, e.g., gold, is sensitive to shocks and volatile and possesses a relatively quiet cycle. When Bitcoin has an asymmetric impact, Bitcoin and gold can equally treat transactions.
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.
Bitcoin was the first cryptocurrency introduced as a cryptographic proof-based electronic payment system in 2009. Till now approximately more than 10,000 digital coins are active in the crypto market. Cryptocurrency is a virtual digital asset that uses cryptography and blockchain technology for transaction verification and records maintenance. Its trading is gaining attention due to volatile behavior, decentralized nature, and liquidity in this digital asset. Trading this digital asset provides anonymity and security in transactions. Groundless fluctuations in its price contribute to making its trade risky. Market Prediction of the cryptocurrency is trending because it can reduce the trade loss risk. Data related to this market is vast and publicly available on the internet. It is nearly impossible to infer the market by simple data analysis. Statistical price prediction approaches are less effective due to the absence of seasonality in cryptocurrency market data. Therefore researchers proposed efficient price prediction techniques utilizing statistical, algorithmic, and neural network-based Machine Learning models. This paper provides a detailed literature survey related to the state-of-the-art Machine learning-based prediction methodologies for the market prediction of the digital asset from 2014 to 2022. This research will categorize, summarize, and review the existing research in cryptocurrency market prediction using Machine Learning classifiers. This paper will benefit researchers to be productive in the right direction in the future.
This study aimed to explain the relationship between bitcoin and nonfungible tokens (NFTs) to determine if the NFT is an alternative investment to bitcoin or a complement during oil price uncertainty. The results showed a comovement between NFT and bitcoin prices. However, after excluding the effect of oil prices and using the partial wavelet coherence test, the results changed and the comovements disappeared: bitcoin and NFT became two separate assets that are affected by different variables. Moreover, oil price has more impact on bitcoin than NFT in the medium and long run. However, these results indicate that the change in oil prices, to some extent, is not considered a strong influence on the crypto market. Nevertheless, a significant rise in crude oil prices leads to a significant change in the comovement between crypto assets and they become interrelated.
Production control is an essential component of a production system used to monitor and manage production orders. Centrally controlled production systems rely on basic data management, such as storing the bill of materials and required work schedules. Components of customer orders are added to generate the corresponding production orders. However, this type of production control can pose serious problems in case of unforeseen events such as machine failure or changing customer requirements during production. In such scenarios, costly and time-consuming re-scheduling becomes necessary, which endangers the company's competitive position and leads to data retention issues. This paper introduces a new method of production control based on the concept of smart orders. Smart orders use smart contracts for autonomous routing based on programmed information in the source code, enabling self-control through the production system. Smart contracts are transaction programs that work with ‘if-then logic”, allowing for partially flexible process schedules and reducing the need for human intervention. The smart order uses the functionalities of blockchain technology, such as security, transparency, and immutability, to ensure the integrity of production data. The paper concludes by presenting the expected value propositions of smart order-based production control on the production system. Depending on the production control system used, companies can integrate the results presented in this paper into their own blockchain implementation strategy.
After the invention of Bitcoin by a man named Satoshi Nakamoto along with other blockchain-based person-to-person payment systems, the cryptocurrency market has instantly gained popularity. Because of this, that is, the volatility of the various cryptocurrency prices. This attracts much attention from both the investors and the researchers. The task of forecasting the prices of crypto-currencies because of the static prices and the arbitrary effects in the market is quite challenging. Cryptocurrency price forecasting models that are available now mainly focus on analyzing extrinsic factors, like macro-financial indicators, data linked to the blockchain, and data from social media – with the goal of enhancing the prediction accuracy. However, the intrinsic noise present in the raw data, caused by market and political conditions worldwide, is complex to interpret. In our research we propose a multiple input convolutional neural network model, specifically a convolutional neural network model for the prediction of future cryptocurrency price. Generally, RNNs and LSTMs are used for problems dealing with timeseries data. We used the concept of residual networks on 1-Dimensional convolutional networks to solve the problem of predicting the price of Bitcoin, the most popular cryptocurrency out there at the moment. Furthermore, we conduct additional experiments on ether, the cryptocurrency of Ethereum to further confirm that even CNNs can work equally well, if not better in comparison to the widely used LSTM neural network models.
The Merkle trees are a type of structure that provides for the efficient and secure authentication of vast amounts of data and is a key component of blockchain technology. This is used in distribution systems to ensure that data is authenticated effectively. This technology is used by Ethereum and Bitcoin. It is incredibly efficient because it uses hashes instead than whole files. The Merkle tree is an essential component of blockchain technology. It’s an arithmetic data structure made up of hashes of various data blocks that serves as a summary of all transactions in a block. It also enables well-organized and secure content verification in large amounts of data. It also aids in validating the data’s consistency and content. Merkle Trees are used by Bitcoin and Ethereum.
Open access
Advanced Steganography and Watermarking Techniques
One of the most important inventions and innovative developments playing an important role in the business world today is blockchain technology.Blockchain technology can be defined as a peer-to-peer distributed ledger database that is verifiable by parties and cannot be permanently updated.The hereditary features like data integrity and immutability in Blockchain is to optimize the processing model in several domains, such as financial services, healthcare, educational system, IoT, supply chain and many more.A decentralized blockchain system in which products are anti-counterfeit and manufacturers can use this system to supply genuine products without managing directly operated stores.The main objective of this study is to present a detailed overview of the use of blockchain technology in supply chain management systems.The present study examines all the relevant research done in the field related to the application of blockchain technology in supply chain operations.
Mohammed Alsaqer, Majid H. Alsulami, Rami N. Alkhawaji, Abdulellah A. Alaboudi
Blockchain technology has revolutionized conventional trade. The success of blockchain can be attributed to its distributed ledger characteristic, which secures every record inside the ledger using cryptography rules, making it more reliable, secure, and tamper-proof. This is evident by the significant impact that the use of this technology has had on people connected to digital spaces in the present-day context. Furthermore, it has been proven that blockchain technology is evolving from new perspectives and that it provides an effective mechanism for the intelligent transportation system infrastructure. To realize the full potential of the accurate and efficacious use of blockchain in the transportation sector, it is essential to understand the most effective mechanisms of this technology and identify the most useful one. As a result, the present work offers a priority-based methodology that would be a useful reference for security experts in managing blockchain technology and its models. The study uses the hesitant fuzzy analytical hierarchy process for prioritizing the different blockchain models. Based on the findings of actual performance, alternative solution A1 which is Private Blockchain model has an extremely high level of security satisfaction. The accuracy of the results has been tested using the hesitant fuzzy technique for order of preference by similarity to the ideal solution procedure. The study also uses guidelines from security researchers working in this domain.
Abstract Blockchain (BC) represents a disruptive technology that has been extensively used to ensure immutability of digital transactions. Starting as an underlying mechanism in the digital currency sector, it has been applicable in a wide range of sectors and application domains. Agricultural sector represents a sector of significance for overall sustainability challenges that is benefiting from digitalization and technological evolution and the enforcement of Industry 4.0 paradigm shift towards precision agriculture. Introduction of Internet of Things, and Cyber-Physical Systems increase overall complexity, with Big Data analysis and Machine Learning technologies providing innovative applications. BC appears to be a promising technology for agriculture providing mechanisms for tracing of products and overall agricultural supply chain (SC) management from the farm to the fork. Authors investigate the challenges and open issues for the application of BC in agriculture performing a state of the art analysis along the PESTELS framework. A large number of challenges including technological ones, creates big research potential for the evolution of the area.
In recent years, machine learning has achieved good results in the field of asset prices. Compared with traditional data analysis and technical analysis, using machine learning methods can show unique advantages in various aspects. In this paper, we combine the correlation between bull and bear mark
In this paper, we present the design, implementation, and evaluation of a secure sensing data processing and logging system. The system is inspired and enabled by blockchain. In this system, a public blockchain is used as immutable data store to store the most critical data needed to secure the system. Furthermore, several innovative blockchain-inspired mechanisms have been incorporated into the system to provide additional security for the system’s operations. The first priority in securing sensing data processing and logging is admission control,i.e., only legitimate sensing data are accepted for processing and logging. This is achieved via a sensor identification and authentication mechanism. The second priority is to ensure that the logged data remain intact overtime. This is achieved by storing a small amount of data condensed from the raw sensing data on a public blockchain. A Merkel-tree based mechanism is devised to link the raw sensing data stored off-chain to the condensed data placed on public blockchain. This mechanism passes the data immutability property of a public blockchain to the raw sensing data stored off-chain. Third, the raw sensing data stored off-chain are secured with a self-protection mechanism where the raw sensing data are grouped into chained blocks with a moderate amount of proof-of-work. This scheme prevents an adversary from making arbitrary changes to the logged data within a short period of time. Fourth, mechanisms are developed to facilitate the search of the condensed data placed on the public blockchain and the verification of the raw sensing data using the condensed data placed on the public blockchain. The system is implemented in Python except the graphical user interface, which is developed using C#. The functionality and feasibility of the system have been evaluated locally and with two public blockchain systems, one is the IOTA Shimmer test network, and the other is Ethereum.
Rohan Maheshwari, Sriram Praveen V A, G Shobha, Jyoti Shetty · 6 authors
A key motivator for the usage of cryptocurrency such as bitcoin in illicit activity is the degree of anonymity provided by the alphanumeric addresses used in transactions. This however does not mean that anonymity is built into the system as the transactions being made are still subject to the human element. Additionally, there is around 400 Gigabytes of raw data available in the bitcoin blockchain, making it a big data problem. HPCC Systems is used in this research, which is a data intensive, open source, big data platform. This paper attempts to use timing data produced by taking the time intervals between consecutive transactions performed by an address and make an identification of the nature of the address (illegal or legal). With the use of three different goodness of fit run tests namely Kolmogorov–Smirnov test, Anderson-Darling test and Cramér–von Mises criterion, two addresses are compared to find if they are from the same source. The BABD-13 dataset was used as a source of illegal addresses, which provided both references and test data points. The research shows that time-series data can be used to represent transactional behaviour of a user and the algorithm proposed is able to identify different addresses originating from the same user or users engaging in similar activity.