S. Muthurajkumar, A. Vignesh, S. Kugan, R. Arunsha
On the Internet, web applications are served from a centralized location i.e., server, for higher maintainability. However, in the centralized architecture, if there is an occurrence of server failure or crash, the web applications cannot be serve to the end-users until the server goes live again. In addition, in the existing centralized architecture for web hosting services, integrity of the hosted websites entirely relies on the third-party applications which checks for any possible threats in the system. In order to provide data integrity within the system and to overcome the above-mentioned single point of failure, we proposed the decentralized solution for hosting web applications, which provides more data availability to the end-users and maintains the integrity of the data. The proposed model makes use of the Interplanetary File System (IPFS) for storing and retrieving web applications, which provides high availability and reliability. In addition, the proposed model uses the Blockchain Technology for authenticity and confidentiality. The smart contracts are deploy on the Ethereum Block chain, which aids the service provider to manage the hosting service system. The proposed model also comparatively decreases the time taken to transfer the file over the IPFS using optimal path-finding algorithm. The proposed algorithm has a lesser time complexity when compared to the Bitswap protocol used in IPFS. The use of blockchain with IPFS cumulatively provides better authenticity via Ethereum Smart Contracts, which reduces risk and failure.
Predicting stock market movements has always been of great interest to investors and an active area of research. Research has proven that popularity of products is highly influenced by what people talk about. Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts. Our objective is to integrate sentiment of social media data with historical stock data and study its effect on closing prices using time series models. We carried out rigorous experiments and deep analysis using multiple deep learning based models on different datasets to study the influence of posts by executives and general people on the close price. Experimental results on multiple stocks (Apple and Tesla) and decentralised currencies (Bitcoin and Ethereum) consistently show improvements in prediction on including social media data and greater improvements on including executive posts.
Blockchain is a shared decentralized database. Distributed among a network of nodes, the term distributed ledger technology (DLT) is also used to describe a system in which transactions are recorded from multiple places at the same time (on different nodes in the network) without a central data store. Blockchain is an advanced product that stores data securely and reliably on the network, and several researchers in the field of blockchain, have expanded the capabilities and capabilities of the Bitcoin blockchain technology with the spread of the Ethereum blockchain platform, which added the capabilities of smart contracts to Bitcoin, smart contracts are Self-executing agreements are tokenized within the blockchain in the same way that users exchange bitcoins. Contracts are "smart" because they can carry out obligations without the intervention of a third party. In this research, we address the governance of the blockchain by defining the term governance, its use and types.
GasTokens are created by smart contracts executing in the Ethereum blockchain to support a form of transaction fee pre-payment. Even though GasTokens had a period of significant activity on Ethereum (regularly surpassing half of the overall weekly smart contract creation and destruction activity), few systematic studies have been conducted on GasToken usage and its effect on the blockchain network. This thesis focuses on understanding the adoption and impact of GasTokens on Ethereum from 2020-5-25 to 2022-5-31. We analyze the usage patterns, including the creation, destruction, and holding of GasTokens. We report several price findings related to GasTokens, and compare the fees paid by users submitting transactions that create and destroy GasTokens to the fees paid by all the transactions in the network over different time periods. We also report on the impact of GasToken creations and destructions on the major mining pools.
David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Juliana Ngozi Ndunagu
Cryptocurrency is an advanced digital currency that is secured by encryption, making it nearly impossible to forge or duplicate. Many cryptocurrencies are blockchain-based with decentralized networks. The prediction of cryptocurrency prices is a very difficult task because of the absence of an appropriate analytical basis to substantiate their claims. Cryptocurrencies are also dependent on several variables, such as technical advancement, internal competition, market pressure, economic concerns, security, and political considerations. This paper proposed the hybrid walk-forward ensemble optimization technique and applied it to predict the daily prices of fifteen cryptocurrencies, such as Cardano (ADA-USD), Bitcoin (BTC-USD), Dogecoin (DOGE-USD), Ethereum Classic (ETC-USD), Chainlink (LINK-USD), Litecoin (LTC-USD), NEO (NEO-USD), Tron (TRX-USD), Tether (USDT-USD), NEM (XEM-USD), Stellar (XLM-USD), Ripple (XRP-USD), and Tezos (XTZ-USD). A performance comparison of these cryptocurrencies was done using classical statistical models, machine learning algorithms, and deep learning algorithms on different cryptocurrency time series. Simulation results show that our proposed model performed better in terms of cryptocurrency prediction accuracy compared to the classical statistical model and machine and deep learning algorithms used in this paper.
Saqib Ali, Waseem Anwar, Basim Juma Salem, Mohammed Al Dhuhli
In recent years, blockchain technology has emerged and gained considerable attention due to its reliability and secure data sharing capabilities in various domains such as supply chain management, finance, the internet of things, agriculture, food, healthcare, and pharmaceutical industries. The track of pharmaceutical supply chains has become more critical in the health society in any country or region to ensure the proper trackability and safety of medicines. With time, it has become more complex and costly to track thousands of various medical products. Without a proper supply chain system, there is a possibility of the availability of counterfeit drugs. Building a traceability system over a blockchain platform would provide a trusted and secured method to track and trace the medical supply chain from the manufacturer to the patient. This work proposes a traceability system for pharmaceutical products in Oman using blockchain technology to maintain the integrity of the data of such sensitive valuable products. In addition to this, different pharmaceutical workflows have been designed and implemented using the Ethereum blockchain platform.
Since executing a smart contract on the Ethereum blockchain costs money (measured in gas ), smart contract developers spend significant effort in reducing gas usage. In this paper, we propose a new technique for reducing the gas usage of smart contracts by changing the underlying data layout. Given a smart contract P and a type-level transformation, our method automatically synthesizes a new contract P ′ that is functionally equivalent to P . Our approach provides a convenient DSL for expressing data type refactorings and employs program synthesis to generate the new version of the contract. We have implemented our approach in a tool called Solidare and demonstrate its capabilities on real-world smart contracts from Etherscan and GasStation. In particular, we show that our approach is effective at automating the desired data layout transformation and that it is useful for reducing gas usage of smart contracts that use rich data structures.
The second revolution in blockchain technology is smart contracts. Smart contracts are used in most of the blockchain applications like cryptocurrency, Health care, banking sectors, supply chain and IOT with different platforms like Fabric, Ethereum, Corda etc. In Ethereum blockchain, due to lack of inefficiency of the knowledge of technical developers and insecure programming languages for smart contracts, the attackers have exploited the smart contracts and the end users have lost millions of dollars like re-entrancy, king of ether throne attack, DoS, forcefully send ethers, multisig wallet, unexpected ether and poly network attack etc. In the year 2016, the attackers have exploited approximately $289 million US dollars with the help of re-entrancy vulnerability. The attackers have also attacked the smart contracts and broke the execution of that particular contracts through king of ether throne attack. In this paper, we propose a novel prevention and detection mechanisms for re-entrancy and king of ether throne attacks using time mechanisms and also implementing the same with proof of concepts for these vulnerabilities.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Stefanos Chaliasos, Arthur Gervais, Benjamin Livshits
The Solidity programming language is the most widely used language for smart contract development. Improving smart contracts’ correctness, security, and performance has been the driving force for research in vulnerability detection, program analysis, and compiler techniques for Solidity. Similar to system-level languages such as C, Solidity enables the embedding of low-level code in programs, in the form of inline assembly code. Developers use inline assembly for low-level optimizations, extending the Solidity language through libraries, and using blockchain-specific opcodes only available through inline assembly. Nevertheless, inline assembly fragments are not well understood by an average developer and can introduce security threats as well as affect the optimizations that can be applied to programs by the compiler; it also significantly limits the effectiveness of source code static analyzers that operate on the Solidity level. A better understanding of how inline assembly is used in practice could in turn increase the performance, security, and support for inline assembly in Solidity. This paper presents a large-scale quantitative study of the use of inline assembly in 6.8M smart contracts deployed on the Ethereum blockchain. We find that 23% of the analyzed smart contracts contain inline assembly code, and that the use of inline assembly has become more widespread over time. We further performed a manual qualitative analysis for identifying usage patterns of inline assembly in Solidity smart contracts. Our findings are intended to help practitioners understand when they should use inline assembly and guide developers of Solidity tools in prioritizing which parts of inline assembly to implement first. Finally, the insights of this study could be used to enhance the Solidity language, improve the Solidity compiler, and to open up new research directions by driving future researchers to build appropriate methods and techniques for replacing inline assembly in Solidity programs when there is no real necessity to use it.
Virtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency investments, accurate price prediction is essential. In view of the fact that the price prediction is a time series task, a hybrid deep learning model is proposed to predict the future price of the cryptocurrency. The hybrid model integrates a 1-dimensional convolutional neural network and stacked gated recurrent unit (1DCNN-GRU). Given the cryptocurrency price data over the time, the 1-dimensional convolutional neural network encodes the data into a high-level discriminative representation. Subsequently, the stacked gated recurrent unit captures the long-range dependencies of the representation. The proposed hybrid model was evaluated on three different cryptocurrency datasets, namely Bitcoin, Ethereum, and Ripple. Experimental results demonstrated that the proposed 1DCNN-GRU model outperformed the existing methods with the lowest RMSE values of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset.
With exponential growth of digital consumer purchasing behavior and the increasing implementation of IoT in retail industry, it has become imperative to provide secure, scalable, and manageable data models to the complex interconnected supply chain management systems.Blockchain technology in recent times has huge potential for deployment of secured data framework over a distributed architecture.In this paper, we have proposed a resilient Supply-Chain Management (SCM) system using Ethereum-based blockchain deployment on a distributed computing test-bed.An IoT-based Radio Frequency Identification (RFID) method is used in our use-case prototype for product information tracking at functional nodes (Manufacturers, Distributors, Retailers and Consumers) of SCM implementation.We have implemented Ethereum T M based blockchain using S olidity T M smart contracts with the help of Remix T M Web-based Integrated Development Environment (IDE).A python-based PyAutoGui is used for automation in blockchain deployment at multiple functional nodes distributed across the supply chain.As proof of concept, we have created a test-bed having 28 Oracle virtual machines (VMs) using Linux for successful implementation and validation of our proposed blockchain based SCM system.We have presented comprehensive results showing Ethereum-blockchain containing complete product information, transactions and tracking for secured information access with distributed digital ledger technology on our test-bed implementation.
This research paper puts emphasis on using cloud computing with Blockchain (BC) to improve the security and privacy in a cloud. The security of data is not guaranteed as there is always a risk of leakage of users’ data. Blockchain can be used in a multi-tenant cloud environment (MTCE) to improve the security of data, as it is a decentralized approach. Data is saved in unaltered form. Also, Blockchain is not owned by a single organization. The encryption process can be done using a Homomorphic encryption (HE) algorithm along with hashing technique, hereby allowing computations on encrypted data without the need for decryption. This research paper is composed of four objectives: Analysis of cloud security using Blockchain technology; Exceptional scenario of Blockchain architecture in an enterprise-level MTCE; Implementation of cipher-text policy attribute-based encryption (CP-ABE) algorithm; Implementation of Merkle tree using Ethereum (MTuE) in a Multi-tenant system. Out of these four objectives, the main focus is on the implementation of CP-ABE algorithm. CP-ABE parameters are proposed for different levels of tenants. The levels include inner tenant, outer tenant, Inner-Outer-Tenant, Inner-Outer-External-Tenant, Outer-Inner-Tenant, External-Outer-Inner-Tenant and the parameters such as token, private key, public key, access tree, message, attribute set, node-level, cipher-text, salting which will help in providing better security using CP-ABE algorithm in a multi-tenant environment (MTE) where tenants can be provided with different levels of security and achieved 92 percentage of authenticity and access-control of the data.
Abstract Simulations of molecules have recently been performed directly on a blockchain virtual computer at atomic resolution. This tutorial review covers the current applications of blockchain technology for molecular modeling in physics, chemistry, and biology, and provides a step‐by‐step tutorial for computational scientists looking to use blockchain computers to simulate physical and scientific processes in general. Simulations of carbon monoxide have been carried out using molecular dynamics software on the Ethereum blockchain in order to facilitate the tutorial.
Open access
Blockchain Technology Applications and Security
Innovative Microfluidic and Catalytic Techniques Innovation
Gold and Silver Nanoparticles Synthesis and Applications
Anupama Ray, Sai Sakunthala Guddanti, Vishnu Ajith, Dhinakaran Vinayagamurthy
Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to Ethereum network being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is still hard. This motivated us to build a hybrid system of quantum-classical algorithms that improves phishing detection in financial transaction networks. This paper presents a classical ensemble pipeline of classical and quantum algorithms and a detailed study benchmarking existing Quantum Machine Learning algorithms such as Quantum Support Vector Machine and Variational Quantum Classifier. With the current generation of quantum hardware available, smaller datasets are more suited to the QML models and most research restricts to hundreds of samples. However, we experimented on different data sizes and report results with a test data of 12K transaction nodes, which is to the best of the authors knowledge the largest QML experiment run so far on any real quantum hardware. The classical ensembles of quantum-classical models improved the macro F-score and phishing F-score. One key observation is QSVM constantly gives lower false positives, thereby higher precision compared with any other classical or quantum network, which is always preferred for any anomaly detection problem. This is true for QSVMs when used individually or via bagging of same models or in combination with other classical/quantum models making it the most advantageous quantum algorithm so far. The proposed ensemble framework is generic and can be applied for any classification task
Ethereum has received increasing attention as the first blockchain platform to support smart contracts. Data mining has become an important tool for analyzing Ethereum transactions. However, existing methods have the disadvantage of covering partial transactions and being vulnerable to privacy-enhancing techniques. In this paper, we propose a scheme for transaction correlation with the node as an entity, which can cover all transactions while being resistant to privacy-enhancing techniques. Utilizing timestamps relayed from N fixed nodes to describe the network properties of transactions, we cluster transactions that enter the network from the same source node. Experimental results show that our method can determine with 97% precision whether two transactions enter the network from the same source node.
This paper discusses a decentralized finance (DeFi) application called MakerDAO. The Maker Protocol, built on the Ethereum blockchain, enables users to create and hold currency. Current elements of the Maker Protocol are the Dai stable coin, Maker Vaults, and Voting. MakerDAO governs the Maker Protocol by deciding on key parameters (e.g., stability fees, collateral types and rates, etc.) through the voting power of Maker (MKR) holders. The Maker Protocol is one of the largest decentralized applications (DApps) on the Ethereum blockchain and is the first decentralized finance (DeFi) application to earn significant adoption. The objective of this paper is to analyze and discuss the significance, uses, and functions of this DeFi application.
With the development of Web 3.0 which emphasizes decentralization, blockchain technology ushers in its revolution and also brings numerous challenges, particularly in the field of cryptocurrency. Recently, a large number of criminal behaviors continuously emerge on blockchain, such as Ponzi schemes and phishing scams, which severely endanger decentralized finance. Existing graph-based abnormal behavior detection methods on blockchain usually focus on constructing homogeneous transaction graphs without distinguishing the heterogeneity of nodes and edges, resulting in partial loss of transaction pattern information. Although existing heterogeneous modeling methods can depict richer information through metapaths, the extracted metapaths generally neglect temporal dependencies between entities and do not reflect real behavior. In this paper, we introduce Time-aware Metapath Feature Augmentation (TMFAug) as a plug-and-play module to capture the real metapath-based transaction patterns during Ponzi scheme detection on Ethereum. The proposed module can be adaptively combined with existing graph-based Ponzi detection methods. Extensive experimental results show that our TMFAug can help existing Ponzi detection methods achieve significant performance improvements on the Ethereum dataset, indicating the effectiveness of heterogeneous temporal information for Ponzi scheme detection.
The thesis addresses the problem of scalability in decentralized blockchains in the context of the trade-off between transaction throughput and hardware requirements to participate in the network. Rollups are presented, that is technologies to verify on-chain blocks executed off-chain by minimizing the assumptions of trust. The variant of the Optimistic Rollups, in particular of Optimism and the use of invalidity proofs through interactive binary search and of the Validity Rollups, in particular of StarkNet, and the use of validity proofs through STARKs are discussed. Finally, the two solutions are compared on withdrawal time, on the cost of transactions and techniques to minimize it, on the possibility of applying the technology recursively, on compatibility with Ethereum and on the licenses used.
The project's goal is to create an application that assures consumers don't have to rely solely on merchants to determine whether or not products are authentic. It also assures that producer may use this system to supply genuine products without having to run direct operated outlets, lowering product quality assurance costs dramatically. The trade in counterfeit goods is expanding, hurting the sales and earnings of businesses who are affected. This project proposes a fully-functional blockchain system to prevent product counterfeiting to ensure the identification and traceability of real products throughout the supply chain. Establishments only need to pay minimal transaction fees, and they no longer need to be concerned about the probability of obtaining counterfeit products. This project utilizes Ethereum blockchain and Remix to develop the application code in the form of a Smart Contract. For testing and implementation, Ganache and MetaMask are used, which simulates the blockchain environment and user account respectively. The Client-Side application leverages with Reactjs and Web3js to interact with smart contract and provide its functionalities to the users, maintaining the integrity of the system. Key Words: Ethereum, Anti-Counterfeit, Solidity, Web3js, Reactjs, Ganache
This paper aims to analyze the volatility spillover relationship between cryptocurrencies and stablecoins dynamically. Within the scope of the study, the daily closing price data of Bitcoin (BTC), Ethereum (ETH), BNB cryptocurrencies, and Tether (USDT) and USD Coin (USDC) stablecoins covering the period from January 1, 2019 to April 6, 2022 was analyzed using the Q-VAR model. Our results suggest that the volatility spillover between the cryptocurrency and stablecoins increased during the Covid-19 pandemic. Moreover, the direction and severity of volatility spillover between cryptocurrencies and stablecoins are affected by global events. While the relationship between cryptocurrencies and stablecoins themselves is strong, the relationship between each other is weak. Our findings suggest that global events influence the interaction between crypto-assets and that cryptocurrencies and stablecoins can be good diversifiers for each other. These findings have important implications for financial market regulators, portfolio investors, and academic research.
Smart contracts running on public blockchains are permissionless and decentralized, attracting both developers and malicious participants. Ethereum, the world’s largest decentralized application platform on which more than 40 million smart contracts are running, is frequently challenged by smart contract vulnerabilities. What’s worse, since the homogeneity of a wide range of smart contracts and the increase in inter-contract dependencies, a vulnerability in a certain smart contract could affect a large number of other contracts in Ethereum. However, little is known about how vulnerable contracts affect other on-chain contracts and which contracts can be affected. Thus, we first present the contract dependency graph (CDG) to perform a vulnerability analysis for Ethereum smart contracts, where CDG characterizes inter-contract dependencies formed by DELEGATECALL-type internal transaction in Ethereum. Then, three generic definitions of security violations against CDG are given for finding respective potential victim contracts affected by different types of vulnerable contracts. Further, we construct the CDG with 195,247 smart contracts active in the latest blocks of the Ethereum and verify the above security violations against CDG by detecting three representative known vulnerabilities. Compared to previous large-scale vulnerability analysis, our analysis scheme marks potential victim contracts that can be affected by different types of vulnerable contracts, and identify their possible risks based on the type of security violation actually occurring. The analysis results show that the proportion of potential victim contracts reaches 14.7%, far more than that of corresponding vulnerable contracts (less than 0.02%) in CDG.
Çalışmanın temel amacı; son on yıla damga vuran kripto paralar arasındaki getiri ve risk açısından nedensellik ilişkisini tespit etmektir. Bu amaçla piyasa değeri en yüksek 10 kripto paradan en fazla verisi bulunan Bitcoin, Ethereum, Litecoin, Stellar, Ripple arasında 10 model oluşturulmuş ve her model, Granger nedensellik ve Hafner-Herwatz varyansta nedensellik testleri test edilmiştir. Çalışmada 23 Şubat 2017 ile 18 Haziran 2021 tarihleri arasındaki günlük verilerden oluşan 1577 gözlem kullanılmıştır. Nedensellik analizi sonuçlarına göre seçili kripto paralar arasında ortalamada Ethereum - Litecoin hariç diğer değişkenler arasında Granger nedensellik ilişkisi, varyansta ise Bitcoin - Ethereum ve Bitcoin - Litecoin hariç diğer değişkenler arasında varyansta nedensellik ilişkisi tespit edilmiştir.
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Non-Fungible Tokens (NFTs) have quickly become an important part of the blockchain economy, theoretically representing ownership of a digital asset registered on a public blockchain such as Ethereum. While several applications of this technology exist, the key underlying factor in NFTs’ success is in their potential for investment – buying, selling, and trading the digital assets such as artwork or video game items using cryptocurrency. The rise and mid-2022 crash of NFT and associated crypto markets have shown the volatility of the sector, and questions have been raised around the sustainability, environmental impact, and exploitative practices within this space – and whether there are, in fact, any possible socially responsible use cases for NFTs. This paper aims to fill a gap in the literature surrounding NFTs, primarily through a thorough ethical analysis of the technology and its implementation, deployment, and sustainability. To do this, it uses the Association of Computing Machinery's Code of Ethics and Professional Conduct as a framework for analysis and, following this analysis, makes some recommendations for those wishing to investigate and/or implement NFTs in an ethically responsible manner. The key message is that unless there is absolutely no other way to solve a problem other than using NFTs, then they should not be implemented, as there is currently no ethical use case or means of implementation of NFTs.