Creating a robust supply chain is one of the factors for more sturdy agriculture. Most of the agricultural produce is getting wasted while storing and transporting the goods. AgroString in Section 3 system collects real-time temperature and humidity data from the IoAT edge device and performs secure data storage and transmission through a distributed ledger. Research and studies are being conducted to forecast the availability of clear groundwater with the help of traditional techniques to meet worldwide food requirements. Collecting quality data from various groundwater sites for storing and sharing for further analysis has become a more significant challenge. Our current work, Section 4, G-DaM, increases the value and reliability of groundwater data by implementing Distributed ledger with a public Blockchain, Ethereum, on the edge layer. Agriculture uses 65% of the world's freshwater for farming, half of which goes wasted; the same is the scenario for energy. We design an insurance system called IncentiveChain, which uses a distributed ledger on edge to incentivize farmers whenever they use resources at a needed level to give similar or more agricultural yield in Section 5. In the current research, we address some of the problems in data management and implement state-of-the-art distributed ledger designs and computing capabilities on the edge layer to show performance improvements in data from smart farming.
The secure management and sale of digital assets has become a critical problem in an increasingly digitized environment. This project intends to address this issue by creating decentralized applications (dApps) that use blockchain technology to provide secure and efficient digital asset management, with a focus on NFTs. The NFT marketplace includes secure wallet connections, NFT image creation, minting, the marketplace, and profile management. Solidity-based smart contracts are utilized to create the NFT Marketplace back end, and IPFS is used for storage. The NFT Marketplace's front-end development uses React JSX and the web3js framework, allowing developers to connect to the Ethereum network. NFT Marketplace may assist creators in selling their art through a smart contract system, in which the artwork's ownership transfers to the new owner upon submission of a digital certificate.
During the COVID-19 outbreak, due to the lockdown, all museums and galleries were closed for more than a year. So, the buying and selling of physical arts have dropped. This project aims to overcome this challenge by developing a decentralized application called Decentralized NFT Exchange. The decentralized non-fungible token (NFT)exchange includes features such as secure wallet connections, NFT creations, buying, selling, and profile management. The back-end of the decentralized NFT exchange is implemented using Solidity-based smart contracts, while InterPlanetary File System (IPFS)is used for decentralized storage. Front-end development of decentralized NFT exchange is implemented using React JSX and framework web3.js that helps developers connect to the Ethereum network. Decentralized NFT can help art creators sell their artworks using a smart contract system where the ownership of the work will become the property of the new owner with proof of a digital certificate. The project demonstrates the practical application of blockchain technology in developing decentralized applications (DApps)for secure and decentralized digital asset management. Overall, Decentralized NFT might be a solution for the copyright of work in the future and the project contributes to secure and decentralized digital asset management
Blockchain is a technology that is rapidly gaining prominence and finding applications in various sectors such as banking, supply chain, healthcare, and e-governance. The consensus algorithm employed in a blockchain network is crucial as it directly impacts the network's performance and security. Different consensus techniques exist, including Proof of Work (PoW), Proof of Stake (PoS), Robust Proof of Stake (RPoS), and Delegate Proof of Stake (DPoS), each with its own set of advantages and disadvantages. In this work, we propose a new consensus algorithm called Delegated Proof of Stake with Exponential Back-off (DPoSEB). DPoSEB utilizes a stake-based selection of delegates and employs an exponential back-off technique to mitigate collisions among nodes within the network. Each delegate is assigned a random sleep time, and the node with the shortest wake-up time is chosen to mine the block for that particular round. However, collisions among nodes can still occur. To provide a fair chance for each delegate node, collided nodes are assigned an exponential back-off time. We implement our proposed algorithm on an Ethereum-based private blockchain network. To evaluate the effectiveness of our proposed work, we compare it with existing consensus mechanisms such as PoS (version 2) and Delegated RPOS with downgrading (DDRPOS) using different scenarios in terms of transaction latency, waiting time, and fairness as evaluation metrics. The results reveal that DPoSEB performs better than POS and DDRPOS.
This study introduces the Distributed Drone Reputation Management (DDRM) framework, designed to fortify trust and authenticity within the Internet of Drone Things (IoDT) ecosystem. As drones increasingly play a pivotal role across diverse sectors, integrating crowdsourced drone services within the IoDT has emerged as a vital avenue for democratizing access to these services. A critical challenge, however, lies in ensuring the authenticity and reliability of drone service reviews. Leveraging the Ethereum blockchain, DDRM addresses this challenge by instituting a verifiable and transparent review mechanism. The framework innovates with a dual-token system, comprising the Service Review Authorization Token (SRAT) for facilitating review authorization and the Drone Reputation Enhancement Token (DRET) for rewarding and recognizing drones demonstrating consistent reliability. Comprehensive analysis within this paper showcases DDRM's resilience against various reputation frauds and underscores its operational effectiveness, particularly in enhancing the efficiency and reliability of drone services.
Прогнозування курсу криптовалюти є однією з найбільш досліджуваних тем і викликає інтерес як з наукової, так і з комерційної точки зору. Колись для прогнозування трейдери використовували різні складні формули та графіки, аналізували їх, і на їх основі будували торгові стратегії. Зараз, з появою штучного інтелекту було задіяно різні алгоритми з метою прогнозування руху фондового ринку, включаючи різні криптовалюти. Алгоритми машинного навчання використовуються як для передбачення ціни акцій на близький період, так і для розуміння ринку у довгостроковій перспективі. На даний момент у відкритому доступі досить багато досліджень використання штучного інтелекту для прогнозування ринку з низькою волатильністю, таких як індекси, або акції міжнародних компаній. Проте, все ще мало досліджень описують прогнозування ринку високої волатильності, такого як ринку криптовалют. Ці ринки відомі своєю непередбачуваністю і складністю, що робить їх цікавими для дослідників та трейдерів. У цій статті досліджується застосування методу лінійної регресії для прогнозування ціни на фондовому ринку високої волатильності, такому як ринок криптовалют. Для даного дослідження було обрано криптовалюту Ethereum. Автор аналізує вплив різних вхідних даних, таких як історія цін, обсяги торгівлі та інші технічні індикатори, на результативність моделі. Також, порівнюється вплив різних параметрів моделі на якість результатів і час її тренування. Крім того, пропонується торгова стратегія, побудована на основі передбачень моделі, та описується результат її роботи в симуляції криптовалютного ринку. Аналіз результатів показує переваги та недоліки використання лінійної регресії для передбачення ринку високої волатильності. У підсумку автор визначає доцільність використання такої моделі для реальної торгівлі та пропонує способи покращення точності передбачень.
A. M. Benarous, İ̇hsan Tolga Medeni, Tunç D. Medeni, Vildan Ateş
This study sheds light on the achievements of digital financial technologies and blockchain technology in the stock market. This study aims to examine the relationship between blockchain technology and macroeconomic variables, as well as the impact these variables have on stock market performance. For this, authors used the methodology of correlation and regression analysis, analyzing data on cryptocurrencies, the stock market and key paper exchange rates. The study confirms a significant correlation between blockchain dynamics, particularly cryptocurrency price fluctuations, and stock market performance, indicating that movements in digital asset classes such as Bitcoin and Ethereum have measurable impacts on traditional financial markets. Traditional economic indicators continue to play a crucial role in stock market behavior, with variables like inflation rates and GDP growth showing strong correlations with market performance. The results suggest a complex interplay between blockchain technology and macroeconomic indicators, emphasizing a growing interconnectedness between emerging digital financial products and economic measures. In addition, the findings are particularly relevant for investors, financial analysts, and policymakers, highlighting the need for a holistic market analysis approach that integrates both new technological advancements in blockchain and economic indicators. The study underscores the evolving influence of blockchain technology on traditional stock markets that encompass both new digital assets and economic frameworks. Moreover, further studies could explore the impact of blockchain technology on specific sectors within the stock market, such as technology, finance, and consumer goods.
Data owners seeking to boost processing power, storage, or bandwidth can take advantage of cloud computing services. However, this shift poses new challenges related to privacy and data security. Searchable Encryption (SE), which combines encryption and search techniques, addresses these issues (violation of data users' privacy) by allowing user data to be encrypted, transmitted to a cloud server, and searched using keywords. Despite its benefits, several recent real-world attacks have raised concerns about the security of searchable encryption. Ensuring forward and backward privacy is likely to become a standard requirement in the development of new SE systems. To address these issues, we propose a scheme that exclusively uses symmetric cryptographic primitives, achieving high communication efficiency and forward and backward privacy. In addition, we emphasize improved I/O efficiency because only the results of subsequent updates are loaded when searching. The time required to retrieve results is so significantly reduced compared to existing SE methods that we have shown that our scheme achieves superior efficiency. Moreover, by integrating blockchain network services with cloud services, we have developed a searchable intelligent cryptosystem suitable for lightweight smart devices. In our study conducted on the Ethereum network, we found our method to be both efficient and secure, especially when compared to methods such as PPSE and Jiang. The results indicate that our system delivers results in terms of performance and privacy within dynamic cloud environments making it a solution for protecting confidential information.
Abstract Ethereum, as a leading blockchain platform, has attracted a significant number of practitioners. These practitioners require a platform for communication and collaborative problem‐solving, which led to Ethereum Stack Exchange (ESE), a Q&A site dedicated to Ethereum‐related issues. While the Q&A site facilitates communication among practitioners, it also introduces new challenges. Practitioners adopt code snippets from Q&A sites to address problems encountered. However, the quality of code snippets on ESE remains largely unexplored. Vulnerabilities and gas‐inefficient patterns in ESE may spread to the code in Ethereum and threaten its regular operation. In this article, we conduct an empirical study investigating the distribution of vulnerabilities and gas‐inefficient patterns in ESE. Further, we analyze the potential impact of vulnerabilities and gas‐inefficient patterns from ESE on Ethereum. However, we encounter a problem during the vulnerability and gas‐inefficient pattern detection. Established smart contract analysis tools in the mainstream realm necessitate complete source code files for thorough analysis, while codes on ESE are often incomplete code snippets. To address this, we introduce the AST‐based code clone detection technique to construct detectable files corresponding to code snippets. This enables us to detect vulnerabilities and gas‐inefficient patterns in code snippets. In the end, our findings demonstrate that 11.18% of the contract‐level code snippets and 4.06% of function‐level code snippets in ESE have vulnerabilities. And 27.21% of contract‐level code snippets and 17.89% of function‐level code snippets contain gas‐inefficient patterns. The additional consumption caused by the gas‐inefficient pattern in ESE is approximately $1,695,002. Based on these findings, we provide recommendations for both ESE and its users, aiming to foster collaborative efforts and create a more reliable Q&A site for practitioners.
The article investigated the efficiency of the cryptocurrency market, with a focus on Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Binance Coin (BNB-USD), based on their substantial market capitalization. For this purpose, we observed the daily closing prices spanning from January 2018 to December 2023 and employed a set of robust tests, including the generalized spectral test, automatic portmanteau test, and automatic variance ratio test. The findings of the study reveal a random pattern in price fluctuations, indicating weak form efficiency. Furthermore, we adopted the rolling window approach to investigate whether market efficiency is dynamic or static over time. The empirical result illustrated that the crypto market efficiency remains static over time, except for USDT. In conclusion, the overall empirical results support the notion of the random walk hypothesis, indicating that past price movements offer no predictive insight into future prices. These findings have significant implications for investors, emphasizing the lack of predictive insight from past price movements. Policymakers are urged to establish a robust framework for market integrity and reliable price discovery.
The Internet of Things (IoT) has been deployed in a vast range of applications with exponential increases in data size and complexity. Existing forensic techniques are not effective for the accuracy and detection rate of security issues in IoT forensics. Cyber forensic comprises huge volume constraints that are processing huge volumes of data in the Information and Communication Technology (ICT) comprised of IoT devices and platforms. Trust blockchain is effective technology those are utilized to assess the tamper-proof records in all transaction in the IoT environment. With the implementation of trust blockchain the record and transaction are processed with a distributed ledger that is managed by the network nodes. The challenge associated with the trust blockchain in IoT forensics is cost and security. To achieve significant cost-effectiveness organizations, need to evaluate the risks and benefits associated with IoT forensics in the trust blockchain technology. In this paper, developed a Block Chain Enabled Cyber-Physical system with distributed storage. The developed Blockchain model is termed as Integrated Hadoop Blockchain Forensic Machin Learning (IHBF-ML). The IHBF-ML model uses the Hadoop Distributed File System (HDFS) with cyberspace to improve security. Within the IHBF-ML model, IoT data communication is established with the smart contract. The smart contract-based blockchain process uses the Machine Learning model integrated with Cat Boost classification model for anomaly detection. Cost in IoT forensic is minimized with the parallel processing of the data through MapReduce Framework for the traffic translation, extraction, and analysis of the dynamic feature traffic from the IoT environment. The experimental analysis stated that constructed IHBF-ML model reduces the cost by ~25% than the other conventional blockchain Ethereum and EOS.
The use of digital certificates is crucial for verifying the authenticity of various credentials in our digital age.However, traditional digital certificate systems suffer from centralization, vulnerability to tampering, and the risk of loss.This paper presents an approach to issuing and managing certificates as Non-Fungible Tokens (NFTs) on the Ethereum blockchain, ensuring their immutability and perpetual existence.The proposed system aims to overcome the limitations of traditional methods by utilizing decentralized storage through the InterPlanetary File System (IPFS) and implementing a robust incentive mechanism for voting and Proof of Stake; the goal is to achieve true permanence and enhanced security for digital certificates.A first phase of a smart contract and front-end interface was achieved, to preserve diplomas and provide a reliable method for verifying their authenticity for employers and other stakeholders.This paper is part of an ongoing effort to develop a robust system, create a prototype to validate the perpetuity assumption, and propose improvements for a global, enduring repository of certificates.
In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.
The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse contract codes to meet various functional needs and have gained widespread use. However, the non-tamperability of smart contracts, coupled with vulnerabilities caused by natural flaws or human errors, has brought unprecedented challenges to blockchain security. Therefore, in order to ensure the healthy development of blockchain technology and the stability of the blockchain community, it is particularly important to study the vulnerability detection techniques for smart contracts. In this paper, we propose a Dual-view Aware Smart Contract Vulnerability Detection Framework named DVDet. The framework initially converts the source code and bytecode of smart contracts into weighted graphs and control flow sequences, capturing potential risk features from these two perspectives and integrating them for analysis, ultimately achieving effective contract vulnerability detection. Comprehensive experiments on the Ethereum dataset show that our method outperforms others in detecting vulnerabilities.
Biometric authentication has been used in applications in various environments as a secure authentication method in computing systems.When combined with blockchain technology, the security of the biometric authentication system can be further enhanced.In this paper, we propose a biometric authentication system that does not expose the original biometric information during the user's biometric authentication process by utilizing a fully homomorphic encryption.In addition, our proposed authentication system utilizes Ethereum's smart contract, which is one of the most famous public blockchains, to record the authentication log between the user and the service provider in a distributed ledger to enhance accountability and traceability.The system is designed to be used only after obtaining the consent of the biometric data subject(user) to comply with the privacy law represented by the European General Data Protection Regulation (GDPR).Finally, we show that the proposed system can process biometric information while maintaining confidentiality, integrity, and accountability of users via security analysis.The cost of maintaining the proposed system is acceptable by analyzing computation time and blockchain maintenance cost.
Blockchain technology, along with cryptocurrencies like Bitcoin, Ethereum, and stablecoin represent potential advancements that could change the financial industry. Stablecoins, have established a presence could change the traditional mechanisms of global economy. Nonetheless, consumer adoption of these technologies has been slightly slow. Many aspects and gaps are still not filled in the literature about these new technologies. In this research, using the technology acceptance model (TAM) to see the factors behind the intention to use cryptocurrencies as a payment method by examining the brand image role in the intention to use cryptocurrencies and blockchain technologies for payment methods. The research has been done through an online survey, distributed between group of people with different backgrounds in Türkiye. The survey was conducted in Turkish, and all scales and measurements have been tested and approved in previous studies. The findings indicate that brand image impacts the intention to use cryptocurrencies and blockchain technology transactions. Also, the strongest relation is between attitude (A) and brand image (BI) these finding highlight the importance of brand image in driving consumer’s attitude and intention to use cryptocurrencies as payment method. Moreover, we discuss in the conclusion.
Both natural and man-made disaster leave thousands of people in vulnerable and in need for essential aid. While individuals generously donate resources, traditional donation management systems suffer from limitations. Centralized control, opaque transactions, and potential corruption often hinder aid delivery and leave victims in despair. This paper proposes a novel Ethereum blockchain-based system for transparent and secure disaster donation management. Utilizing smart contracts, the system ensures traceability, accountability, and immutability of donations, empowering donors and fostering trust. This paper presents the system's architecture, detailing its components and interactions through sequence diagrams and algorithms. Additionally, successful testing on the Sepolia Ethereum testnet validates its functionality. To assess its effectiveness, a cost and security analysis is conducted. This blockchain-based framework offers a promising solution for transparent and efficient disaster response, potentially revolutionizing donation management. Further research, particularly on donation allocation optimization within the system holds immense potential for future development.
This study analyzes the application of code-generating Large Language Models in the creation of immutable Solidity smart contracts on the Ethereum Blockchain. Other works have previously analyzed Artificial Intelligence code generation abilities. This paper aims to expand this to a larger scope to include programs where security and efficiency are of utmost priority such as smart contracts. The hypothesis leading into the study was that LLMs in general would have difficulty in rigorously implementing security details in the code, which was shown through our results, but surprisingly generally succeeded in many common types of contracts. We also discovered a novel way of generating smart contracts through new prompting strategies.
Abstract Decentralized autonomous organization (DAO)-based applications can revolutionize traditional centralized decision-making procedures and services by enabling scalable, decentralized, autonomous, and democratized decision-making processes. Unlike traditional applications, DAO-based decentralized applications use Ethereum blockchain-based smart contract programs to execute policies or make automated decisions. To that end, 6 G technologies can improve the latency and reliability of many existing blockchain-based DAOs. Previous research did not investigate the execution of DAO and non-DAO-based applications, as well as an adaptive resource choreography scheme, while accounting for various 6 G technologies, blockchain, and mobile-edge cloud (MEC). To prevail over the previous shortcomings, this article supplies a street smart multi-platform coordination, application scheduling, and low-latency-aware resource choreography scheme for both DAO and non-DAO-based application execution over blockchain and MEC-enabled 6 G networks by taking heterogenous DAO and non-DAO application count, application requirements, physical worker, virtual worker, and communication resource status into account. The results verified that the proposed kaizen scheme delivers at least 11.26% app work completion delay gain, 7.2% user energy overhead gain, 6.55% user economic charge gain, and 21% service provider profit than the compared schemes.
Qi Liang, Ning Shi, Yu‐an Tan, Chunying Li · 5 authors
With the widespread adoption of blockchain technology, its public ledger characteristic enhances transaction transparency but also amplifies the risk of privacy breaches. Attackers can infer users’ real identities and behaviors by analyzing public transaction patterns and address relationships, posing a severe threat to users’ privacy and security, and thus hindering further advancements in blockchain applications. To address this challenge, covert communication has emerged as an effective strategy for safeguarding the privacy of blockchain users and preventing information leakage. But existing blockchain-based covert communication schemes rely solely on the immutability of blockchain itself for robustness and suffer from low transmission efficiency. To tackle these issues, this paper proposes a stealthy communication model with blockchain smart contract for bidding systems. The model initiates by preprocessing sensitive information using a secret-sharing algorithm-the Shamir (t, n) threshold scheme-and subsequently embeds this information into bidding amounts, facilitating the covert transfer of sensitive data. We implemented and deployed this model on the Ethereum platform and conducted comprehensive performance evaluations. To assess the stealthiness of our approach, we employed a suite of statistical tests including the CDF, the Kolmogorov–Smirnov test, Welch’s t-test and K–L divergence. These analyses confirmed that amounts carrying concealed information were statistically indistinguishable from regular transactions, thus validating the effectiveness of our solution in maintaining the anonymity and confidentiality of information transmission within the blockchain ecosystem.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Giovanni Quattrocchi, Filippo Scaramuzza, Damian A. Tamburri
Bitcoin and Ethereum, respectively the first and the second generations of blockchains, exhibit two main problems, mostly connected to the increase of network traffic and load onto their respective networking and service models: scalability and interoperability. To solve these issues, several technologies have been introduced—thus paving the way to the so-called third-generation blockchains—which are divided into three main categories: (1) Layer 1 solutions, (2) rollups, and (3) side-chains. We present a validated framework for the evaluation and comparison of these categories, based on the three main non-functional aspects—reflecting therefore a trilemma—that discriminate their use for the design and orchestration of complex blockchain-oriented service applications, namely: scalability, decentralization, and security.
This paper presents a case study of a cryptocurrency scam that utilized coordinated and inauthentic behavior on Twitter. In 2020, 143 accounts sold by an underground merchant were used to orchestrate a fake giveaway. Tweets pointing to a fake blog post lured victims into sending Uniswap tokens (UNI) to designated addresses on the Ethereum blockchain, with the false promise of receiving more tokens in return. Using one of the scammer's addresses and leveraging the transparency and immutability of the Ethereum blockchain, we traced the flow of stolen funds through various addresses, revealing the tactics adopted to obfuscate traceability. The final destination of the funds involved two deposit addresses. The first, managed by a well-known cryptocurrency exchange, was likely associated with the scammer's own account on that platform and saw deposits exceeding $3.5 million. The second address was linked to a popular cryptocurrency swap service. These findings highlight the critical need for more stringent measures to verify the source of funds and prevent illicit activities.
Este estudio investiga los efectos del día de la semana en el mercado digital, con un enfoque en bitcoin y ethereum, abarcando desde el 1º de julio de 2020 hasta el 31 de diciembre de 2023, en el período posterior al COVID-19. Empleando pruebas paramétricas y no paramétricas junto con el modelo GARCH (1,1), se analizó la dinámica del mercado. Los hallazgos indican un efecto significativo del día de la semana en ethereum, caracterizado por notables variaciones de rendimiento entre diferentes días, mientras que itcoin no muestra anomalías de calendario discernibles, lo que sugiere una mayor eficiencia del mercado. La susceptibilidad de ethereum a estos efectos subraya las complejidades actuales del mercado. Las disparidades en las anomalías del calendario surgen de la evolución de la dinámica del mercado, las diferencias metodológicas y la naturaleza especulativa del comercio de criptomonedas. Además, el mercado descentralizado y global complica la identificación precisa de los efectos en todo el mercado. Este estudio proporciona evidencia empírica sobre los efectos del día de la semana en el mercado de criptomonedas, lo que facilita a los inversionistas refinar las estrategias comerciales y la gestión de riesgos. Se justifica realizar más investigaciones para explorar los mecanismos subyacentes y monitorear los desarrollos regulatorios y tecnológicos para obtener información de los inversionistas.
Abstract Online voting is gaining traction in contemporary society to reduce costs and boost voter turnout, allowing individuals to cast their ballots from anywhere with an internet connection. This innovation is cautiously met due to the inherent security risks, where a single vulnerability can lead to widespread vote manipulation. Blockchain technology has emerged as a promising solution to address these concerns and create a trustworthy electoral process. Blockchain offers a decentralized network of nodes that enhances transparency, security, and verifiability. Its distributed ledger and non‐repudiation features make it a compelling alternative to traditional electronic voting systems, ensuring the integrity of elections. To further bolster the security of online voting, we propose DemocracyGuard platform on the Ethereum blockchain, which incorporates facial recognition technology to authenticate voters. By leveraging these advancements, DemocracyGuard aims to provide a secure and resilient platform for online voting, paving the way for its broader adoption and revolutionizing the electoral landscape.