Over the years with the advent of technology 4.0, there have been an increased inclination towards the blockchain technology and the cryptocurrencies. Thus it is becoming imperative to implement fraud detection techniques over the Ethereum network which nowadays is a popular platform for the developers to create smart contracts and deploy decentralized apps. Over the years the various machine learning algorithms have been growing for the process of fraud detection and in particular neural networks have shown promising results. As such in this paper, a neural-network based approach has been presented for Ethereum fraud detection and to validate the effects of the performance this proposed model has been compared with its peers. In contrast to the various models such as Logistic Regression, SVM, Gaussian Naive Bayes, K-nearest neighbour, the neural network perform the best providing an accuracy of about 97.09% which is higher than the rest. It is then seen that neural networks are relatively effective in learning complex patterns of the dataset and thus classifying the resultant transaction as genuine or fraudulent. Thus this work contribute in the development of effective solutions for fraud detection in the Ethereum and other blockchain platforms, enhancing their security and reliability.
This paper aims to analyze cryptocurrency volatility by examining the effect of Gold, Dollar Index, and Composite Stock Price Index (IHSG) as independent variables and on Bitcoin and Ethereum as dependent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum, which have the largest market capitalization. The data in this study used the period January 1, 2018, to December 31, 2021. This study used GARCH analysis. This study's results indicate that Bitcoin's volatility is influenced by the price of Bitcoin itself, gold, and the stock exchange index, and Ethereum and the stock exchange index influence Ethereum. This shows that the cryptocurrency market is inefficient as the prices are also affected by past prices.
Due to its qualities that offer unbreakable security and global accessibility, blockchain has grown to become one of the most widely used technologies today. Blockchain technology is revolutionizing the financial and banking sectors, but it isn't only the financial sector; the majority of companies that used online payment systems, faced an issue with transparency or wished to do away with the middleman and also wish to incorporate it. NFT is a further emerging technology that is seen as a subset of the blockchain (NFT stands for the non-fungible token). NFT is merely a digital token that is awarded to any original creation, such as a painting or a song and or a tweet. This study examines the drawbacks of blockchain and NFT as they are faced by several industries, including IoT, banking, music, agriculture, food and supplies, and healthcare.
Abstract Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors. Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud. Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc. Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud. Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.
Ahmad Sani Bello, Jens Schneider, Roberto Di Pietro
Pump and Dump schemes represent a threat to any market. While this issue has long been regulated in mature markets, in unregulated markets, such as crypto exchanges, this plague is very present, and even exacerbated by the low capitalizaton of many cryptocurrencies that represent the perfect target for such a fraudulent scheme. In this paper, we detail a Low Latency Detection solution (LLD) based on deep learning to automatically detect pump and dump activities on centralized cryptocurrency exchanges. We train a LSTM-based auto-encoder on BTC valuations, which can reliably be considered a proxy for regular trading-due to their larger capitalization. We use this auto-encoder to predict valuations on alt coins and use thresholding on a Gaussian tail condition to trigger detection. We argue that low latency detection is paramount for the practicality of such approaches. Unlike previous methods, our solution (LLD) detects the majority of pumps in less than five minutes (2.2 minutes on average) when using OHLCV data at one-minute resolution. In addition, we use social media data only to generate ground truths during testing. We show that in many cases a significant amount of the trade volume could have been saved had LLD been used to trigger trade suspension mechanisms. The idiosyncratic approach of our scheme, its sound rationale and viability, combined with the quality of achieved results-tested over an extensive experimental campaign-and the insights discussed in the paper also pave the way for further research in the field.
Seng Kuang Yap, Zhongli Dong, Mark Toohey, Young Choon Lee · 5 authors
Blockchain technology has attracted significant industry, academic, and governmental attention since its emerged in 2008. Blockchain use cases are now being explored by traditional, transaction-oriented businesses in the finance, insurance, logistics and healthcare sectors to name a few. This has expanded further with the widespread use of Internet of Things (IoT) devices. Massive amounts of data are generated by IoT devices and are recorded in the blockchain. While blockchain provides many advantages, such as immutability and transparency, its serialized nature makes impossible to read in a single query. Multiple requests are required even for simple tasks, such as displaying an account's transaction history. This further leads to the difficulty in understanding the data in the blockchain. In this paper, we address the problem of smart contract visualization in a real-time manner. To this end, we design a visualization dashboard for smart contracts. A visual aid for massive amounts of data helps users understand the blockchain's overall activities, uncover operational risks and provide critical intelligence by visualising unusual activities and connections. Such insights may enable the user to investigate and predict any anomalies or reveal any network vulnerabilities. Cattle farm selected as a use case because the voluminous data can be acquired from IoT sensors on the farm cattle. Our dashboard has been proven to help visualize the life cycle of animals, the distribution of activities and time factor analysis. This visualization can give a user a better perspective of the token functions and results as well as animal management issues.
Nicolás López, Alexander Agbu, Adamson Oloyede, Emmanuel E. Essien · 6 authors
Certain IoT (Internet of Things) device data such as meteorological data are public goods, which are by definition, in high demand by a large user base. Accessibility to these datasets thrives with the use of blockchain technology. However, research has shown that due to the consensus mechanism of existing blockchain platforms, transaction approval delays and high transaction (gas) fees have been a challenge. This paper presents a software tool to integrate and store IoT device data onto a resource-efficient blockchain, with a decentralized proof of stake consensus mechanism, for faster and scalable application, with zero to near-zero transaction fees. • Some small Internet of Things (IoT) data like temperature, pressure, and humidity weather data can be stored on a blockchain. • Node-RED is a tool used by millions of users from large companies like IBM to small hobbyists. • The eosio-push node is custom built to take in IoT and transmit to a blockchain. • It can be run on raspberry pi. • It allows for customization of endpoints and message types. • The software is publicly available on NPM and receives between 10 and 50 downloads per week.
Mr. R. Arunachalam, Myana Santhoshini, R. Tamil Prabha, R. Tamil Prabha
In this paper, we tried to estimate the Bitcoin price precisely taking into consideration various parameters that affect the Bitcoin value. In our work, we pointed to understand and identify daily changes in the Bitcoin market while obtaining insight into most appropriate features surrounding Bitcoin price. We will predict the daily price change with highest possible accuracy. The market capitalization of publicly traded cryptocurrencies is currently above $230 billion. Bitcoin, the most valuable cryptocurrency, serves primarily as a digital store of value, and its price predictability has been well-studied. For the first phase of our investigation, we aim to understand and identify daily trends in the Bitcoin market while gaining insight into optimal features surrounding Bitcoin price. Our data set consists of various features relating to the Bitcoin price and payment network over the course of five years, recorded daily. For the second phase of our investigation, using the available information, we will predict the sign of the daily price change with highest possible accuracy with deep learning algorithm such as long short term memory for greater accuracy. Compared with benchmark results for daily price prediction, we achieve a better performance, with the highest accuracies of the statistical methods and deep learning algorithms. Deep Learning models includes Long Short-Term Memory in RNN for Bitcoin price prediction are superior to statistical methods
B. Subashini, Hemavathi Devarajan, Venkatesh Kaliamoorthy
Blockchains typically employ IPFS for off-chain storage of user information.Centralized management, muddled data, inaccurate data, and the simplicity of building information enclaves plague traditional traceability systems.In this research, blockchain technology is used to record and access data on Non-Perishable (NP) agricultural commodities in the distribution chain to solve the challenges above.The blockchain and IPFS both store public and private data encrypted.This lessens the burden on the blockchain and enhances information search.Blockchain technology enhances farmer-customer relationships and food supply chains by tracking food back to its source.Its secure data storage enables datadriven farming.By storing encrypted files IPFS hashes in smart contracts, IPFS secures agricultural data and addresses the blockchain storage problem.Being deployed in association with connects makes it possible for rapid financial transactions to occur with any changes made to the blockchain's data.This article analyses performance and simulates implementation in Ethereum testnets.The results show that our system protects sensitive data, supply chain data, and real-world applications by increasing the throughput and latency.
Aims: This article investigates recent advancements in machine learning and blockchain technology for cryptocurrency price prediction. The study presents a ML system using various techniques applied to six different datasets. The findings highlight that simpler models can outperform complex ones in predicting cryptocurrency prices. Methods: The methods used in this study include applying diverse ML techniques such as LSTM, CNN, SVM, KNN, XGBoost, Astro ML, LASSO, RIDGE, linear regression, DT, and GP on six cryptocurrency datasets to predict prices. Results: The research evaluated various machine learning techniques for predicting cryptocurrency prices and reported the following RMSE values: Bitcoin prediction using Nadaraya-Watson kernel regression yielded an RMSE of 0.17, while Dogecoin prediction with linear regression resulted in an RMSE of 0.032. Ethereum price prediction using Gaussian regression achieved an RMSE of 0.02. For USD Coin, a combination of XGBoost, Gaussian regression, and Ridge techniques led to an RMSE of 0.014. Binance Coin price prediction using Gaussian regression had an RMSE of 0.032, and finally, Cardano Coin prediction employing LSTM reached an RMSE of 0.059. Conclusion: This study demonstrated the effectiveness of various machine learning techniques in predicting cryptocurrency prices. It revealed that simpler models can outperform complex ones in certain cases. The research contributes valuable insights to the field and can guide future work in cryptocurrency price prediction. The proposed model achieved promising results as evaluated by the RMSE metric.
The possibilities and benefit of using distributed ledger technology for storing and protecting patient medical information is greatest in the health care industry, which is one of the primary industries in this regard. Blockchain technology is being used by several companies. Inorder to protect the security and privacy of its people’ personally identifiable information and to promote the use of blockchain technology, the Government of India (GoI) is eager to digitize. Using the Ethereum blockchain, we provide a solution in this study for the safe keeping of patient medical records (PMR).Our solution provides secure and hasslefree access, storage, and patient medical record sharing. We have developed the front end as well as the back end of our project using TypeScript and Next UI Users need to register themselves if they are visiting for the first time. The information shared by the user will be stored entirely in our backend applications. We have used the IPFS file system for storing and accessing files on the internet. IPFS is a file system that helps to create a permanent and decentralized method for storing and accessing files. First, a user will upload his or her document, and the document will be uploaded to the Inter Planetary File System (IPFS). Once the document is uploaded, a unique hash is returned, which is also known as a CID (content identifier). This is used to uniquely identify the uploaded file on IPFS. Then, that CID is stored in the blockchain as a private variable, so not everyone can access it. When someone can access it, we will specify the conditions.
Yogesh Jain, M. Suryakumar, Sumagna Patnaik, V. Vidya Chellam · 6 authors
The Central bank digital currency plays an important role in the national economic development of a country. The central bank digital currency is adopted with the increase in the development of digital currency and blockchain technology. The management of digital data to adopt transparency and accountability is obtained through the blockchain technology. The blockchain technology is the fundamental attribute for internet of things that performs on the embedded system. The ledger of transaction is visible to both the members involved in the banking system through the blockchain technology. The privacy with transparency in the transaction is obtained through the peer to peer protocol. To increase the data storage with flexibility, accuracy in transactions with transparency, evolving privacy and transparency, the digital currency in commercial bank using blockchain technology is implemented.
The success of blockchain technology in cryptocurrencies reveals its potential in the data management field. Recently, there is a trend in the database community to integrate blockchains and traditional databases to obtain security, efficiency, and privacy from the two distinctive but related systems. In this survey, we discuss the use of blockchain technology in the data management field and focus on the fusion system of blockchains and databases. We first classify existing blockchain-related data management technologies by their locations on the blockchain-database spectrum. Based on the taxonomy, we discuss three types of fusion systems and analyze their design spaces and trade-offs. Then, by further investigating the typical systems and techniques of each type of fusion system and comparing the solutions, we provide insights of each fusion model. Finally, we outline the unsolved challenges and promising directions in this field and believe that fusion systems will take a more important role in data management tasks. We hope this survey can help both academia and industry to better understand the advantages and limitations of blockchain-related data management systems and develop fusion systems that meet various requirements in practice.
As the price of virtual currency fluctuates greatly, precise prediction and appropriate trading strategies can bring investors best returns. This paper predicted the price of Ethereum and Bitcoin in the light of autoregressive integrated moving average model (ARIMA) and get a R2 of 0.995 and 0.993 respectively, which indicates the model can yield reasonable predictions. Then their investment ratios are set to 0.88 and 1.12 respectively by analytic hierarchy process (AHP). Particle swarm optimization (PSO) is used to solve the daily revenue function formed by the predicted price and the current price. Finally, the paper compared the returns yielded by the PSO trading strategy optimized by AHP and the strategy without optimization. It can be concluded that the AHP has a possibility of 64.66 per cent to yield more returns when used.
In general, academic verification of an employee is done manually takes a lot of time, cost and less trustworthy. The trustworthiness of the certificate is dependent on the employee. Due to financial benefits and other advantages, fake certificates are easily created by fraudulent members. Detecting a fraudulent certificate is a hard process. Nowadays, blockchain technology provides wonderful benefits to all sectors for maintaining documents in an immutable manner. Once the data is stored in a blockchain, no one can change or delete that data. Hence, blockchain technology is used to store and verify academic information is highly scalable and guaranteeing the privacy of users’ data. In a proposed work, the blockchain network is created between the educational institutions and employers for efficient information sharing and verification. For unique identification of the certificates QRcode is assigned to each academic certificate. By using SHA256 algorithm the hash value of the QRcode is calculated and has been stored in a block in a distributed ledger. The employer calculates the hash value of the certificate which is submitted by the employee and compared this hash value to the hash value of the certificate stored in the block. If both are equal, the employer identifies it’s a valid certificate or else rejected it’s invalid certificate. Thus, trustworthy verification like confidentiality, authentication, authorization, ownership and privacy is achieved through blockchain technology. Likewise, the certificate verification latency is reduced and throughput is increased in this approach.
Jay Joshi -, Shivani More -, Vineet Kunder -, Karan Patel - · 5 authors
Non Fungible Tokens which are commonly known as NFT’s are digital items such as audios, Videos, Photographs etc. NFT’s are unique cryptographic tokens that exist on a blockchain and cannot be replicated. All Non Fungible Tokens have unique identification codes and metadata that differentiate each token from each other.
In the past few decades, there has been an increasing demand for assets trading with help of machine learning. Contemporarily, the cryptocurrency and gold market has become prosperous with extremely dramatical fluctuations. This paper aims to study the trading price laws based on machine learning scenarios of Bitcoin and Gold to predict the price of the two currencies. To be specific, this study gives an inside view of the application of a method combined three algorithms (i.e., KNN, XGBoost and LightGBM) to predict the future Gold and Bitcoin price browser based on past data from 2017 to 2022. According to the analysis, the study shows the difference of three models, the accuracy of the combined algorithms and proves the related metrics to predict the price of the Gold and Bitcoin. Overall, these results give a guideline for the investor to make sensible decisions about Bitcoin and Gold price and shed light on guiding further exploration of price forecasting in terms of machine learning approaches.
This study investigates the important role that the blockchain plays to manage the information about who did what and when and hence provides a strong base for any legal potential conflicts. Blockchain technology permits you to distribute, encrypt, and secure the records of digital transactions. In addition, bitcoin and other cryptocurrencies are encompassed in it. Even though the construction industry has traditionally been a late user of innovative technology compared to other sectors of the economy, it faces various hurdles in terms of trust, accessibility, information sharing, and process automation. As a result, stakeholders, clients, subcontractors, contractors, and suppliers have been unable to work together effectively. Even if building information modeling is employed, which envisions a centralized building, the primary benefit of blockchain is the secure storage of sensitive sensor data.
Blockchain provides an innovative method for storing information, carrying out functions, executing transactions, and establishing trust. In light of the increasing popularity of digital payments, it is critical to use blockchain technology to carry out transactions safely and to build trust among people. This paper presents my point of view on the role that blockchain technology plays in ensuring the safety of digital currencies. The significance and application of integrating blockchain and digital currency security as part of Industry 4.0 technologies will primarily be utilized in cybersecurity. Despite the Blockchain ledger’s open and distributed nature, the data remains safe and verifiable. As a result, we can state that blockchain technology is essential and have vast scope in coming future. Therefore, it is safe to say that the blockchain technology is revolutionary. Every transaction can be recorded in a way that is both permanent and indestructible. Hacking, data theft, and information loss are all rendered impossible by this digital ledger’s impenetrability.
P. Yuvarani, P Bharani, B Dharun, Savithramma P. Dinesh‐Kumar
Ethereum is one of a technology that allows us to create D-Applications and organizations, keep assets, transact, and communicate without being controlled by a central authority. Investing in cryptocurrencies is now a big business, with tremendous capital flow and billions of industries taking over what was formerly a small market. With this investment, it is critical to grasp the highs and lows of a certain cryptocurrency as well as the output provided by such decisions. Prediction of cryptocurrencies is concrete and needs a thorough comprehension of the daily movement of money. This paper comprises of cryptocurrency price prediction and analysis utilizing the FB-Prophet algorithm, using Ethereum as the cryptocurrency under consideration for analysis and prediction Ethereum. In this paper, we will anticipate the daily closing price series of the Ethereum cryptocurrency using pricing data from previous years (January 2020 to December 2021).
Monika di Angelo, Thomas Durieux, João F. Ferreira, Gernot Salzer
Abstract Blockchain programs (also known as smart contracts) manage valuable assets like cryptocurrencies and tokens, and implement protocols in domains like decentralized finance (DeFi) and supply-chain management. These types of applications require a high level of security that is hard to achieve due to the transparency of public blockchains. Numerous tools support developers and auditors in the task of detecting weaknesses. As a young technology, blockchains and utilities evolve fast, making it challenging for tools and developers to keep up with the pace. In this work, we study the robustness of code analysis tools and the evolution of weakness detection on a dataset representing six years of blockchain activity. We focus on Ethereum as the crypto ecosystem with the largest number of developers and deployed programs. We investigate the behavior of single tools as well as the agreement of several tools addressing similar weaknesses. Our study is the first that is based on the entire body of deployed bytecode on Ethereum’s main chain. We achieve this coverage by considering bytecodes as equivalent if they share the same skeleton. The skeleton of a bytecode is obtained by omitting functionally irrelevant parts. This reduces the 48 million contracts deployed on Ethereum up to January 2022 to 248 328 contracts with distinct skeletons. For bulk execution, we utilize the open-source framework SmartBugs that facilitates the analysis of Solidity smart contracts, and enhance it to accept also bytecode as the only input. Moreover, we integrate six further tools for bytecode analysis. The execution of the 12 tools included in our study on the dataset took 30 CPU years. While the tools report a total of 1 307 486 potential weaknesses, we observe a decrease in reported weaknesses over time, as well as a degradation of tools to varying degrees.
Blockchain technology is becoming widely popular nowadays along with their decentralized peer- to-peer network and its privacy. Bitcoin is also widely storming in the world. Blockchain technology changes the lifestyle of people and business views on many fields through its privacy and security. Many researches were done on this technology because of its security and requirements in various fields of life. In the current era major issues are security on online translation, cloud computing, large data and Blockchain more focus on designing secure service. The objective of writing this review paper is to summarize what Blockchain is and spread awareness about its usage, security and how it works. Key Words: Blockchain, Ethereum, Cryptocurrency, Bitcoin, Consensus Methodor Algorithm, consensus rules, Hash,Genesis Block, Applications of Blockchain, Booming Domain of Blockchain
R. R. Bhalerao, Pratik Gite, Preet Patil, Rahul Gupta · 5 authors
The usage of Electronic Health Records (EHR) is becoming increasingly widespread globally. However, there are several issues with the current EHR systems, particularly in terms of privacy and security. Some of the major challenges faced by the current systems are limited infrastructure, shortage of trained healthcare personnel, lack of funding, limited usability, data privacy, lack of proper planning and limited access to technology. The proposed solution addresses many of these problems by utilizing a public Ethereum blockchain to connect hospitals, patients, and doctors worldwide. By utilizing RSA asymmetric key cryptography, the system ensures secure storage and controlled access to records. Patients are given full control over their records and are able to grant or revoke access for hospitals or healthcare centers. Additionally, the system employs IPFS (Interplanetary File System) for record storage to offer the benefits of being decentralized and providing immutability of records. Furthermore, our model is able to maintain disease statistics while maintaining patient privacy.
Artificial Intelligence and Decision Support Systems
NFT, or non-fungible tokens, are online certificates of ownership that can be traded based on data units stored in digital ledgers belonging to blockchain technology.This is non-fungible, meaning that it cannot be exchanged and is unique.NFT has been around since 2014.But now, it is increasingly being considered as a practical method for trading digital artwork or art.To buy NFT assets, you require special coins in the form of NFT coins, which consist of various types, such as mana coins, sand, axes, and other NFT coins.The NFT coins are used to process NFT purchase transactions.The movement of NFT coins over time is relatively erratic and uncertain.This NFT coin price prediction will be very useful for investors to know how the investment flow of each price works because the price of each NFT coin will change from time to time.through the literature study stage, interviews, and viewing daily NFT coin price data where the attributes used are date, open, high, low, close, and volume.The method used in this research is k-Nearest Neighbours.Dataset collection through the website www.coinmarketcap.comfor the period January 1, 2019 to December 31, 2021.Then the data processing is carried out.An accurate NFT coin price prediction model can help investors in considering transaction decisions because NFT coin prices, which tend to be non-linear, will allow investors to make predictions.This study aims to obtain the predicted value of NFT coins using the k-Nearest Neighbours algorithm.