R. Akila, J. J. Brindha Merin, S. Subhashini, Niyati Kumari Behera · 6 authors
Introduction: the authorities responsible for Land Registration (LR) are often held accountable for the mishandling and forgery of LR documents in many countries. Some individuals may use a cutting-edge technology called Blockchain (BC) to digitally transfer assets such as currency, paperwork, and real estate (RE). Each transaction, monetary exchange, and shared information facilitated by a Peer-to-peer network (P2P) can be carried out through a designated node. Methods: this paper proposes a method for secure transfer of land ownership using BC Technology without the involvement of intermediaries. Buyers and sellers are entering into a land ownership agreement via the Ethereum network. The decentralised systems has been to enhance their reliability. Currently, there is a growing development of decentralised solutions based on blockchain technology to tackle the limitations of centralised systems. Results: the application of BC technology gradually mitigates the security concerns of the LR system. Due to the fact that each block is connected to the hash of the preceding one, each hash value will be unique. The SHA algorithm is employed for this purpose. Conclusion: the ownership of the property cannot be transferred to the customer through the application. However, the smart contracts allow for automated updating of records
Ethereum smart contracts leverage blockchain technology to facilitate the transfer of values directly between participants on a network, eliminating the need for a central authority. These contracts are deployed on decentralized applications that operate on top of the blockchain. By doing so, they provide individuals with the ability to create agreements in a transparent and secure environment, minimizing conflicts and promoting trust. It has been observed that there are bugs in the smart contract’s codes as these are provided by various programmers across the globe. The attackers exploit these security loopholes and pose a significant threat to applications, which subsequently result in financial losses to users. Discovering vulnerability in each contract is an important but time-consuming task. Therefore, we require to provide a security layer to each smart-contract such that it will make the exploitation a bit difficult task for attackers. The use of encryption and obfuscation techniques improves the security layer. The main focus of this research is source code obfuscation, which can increase security by up to 75%. The code obfuscation in security is mainly used by attackers to hide their malicious intent. We, in this approach suggest this method for increasing the complexity of smart contracts so that these cannot be exploited easily. We evaluate the impact of adding security layer to smart contract. The evaluation was done with various static and dynamic tools that identify the vulnerability in smart contracts. We achieved promising results which show that Obfuscation technique enhances the security and complexity of codes up to 75% which are stored on public blockchain.
El trabajo de grado se enfoca en el diseño y aplicación de un modelo de autenticación utilizando la tecnología Blockchain de Ethereum para mejorar la seguridad de los dispositivos IoT hogares como lo son las cámaras de seguridad, teniendo en cuanta que son dispositivos inteligentes con conexión a internet que permiten un control y monitoreo del hogar. Día a día estos dispositivos van en un auge de crecimiento exponencial ampliando el monitoreo y visualización del hogar, compartiendo más y más conexiones sobre el internet, en contraste, los ciberdelincuentes están constantemente ocupados, y cada día surgen nuevas amenazas. Esto significa que una cámara de seguridad adquirida hace cinco años o una comprada apenas hace seis meses podrían presentar una brecha de seguridad. Esto conlleva a que incluso aquellos hackers con poca experiencia puedan identificar exploits en la web y aprovecharlos para infiltrarse en tu red. Mediante el desarrollo del modelo se logró realizar una investigación sobre tres dispositivos de fabricantes diferentes, seleccionando uno de los tres para la prueba de concepto, bajo la ejecución de un objetivo general definido por un modelo de autenticación mediante blockchain Ethereum, y cuatro objetivos específicos orientados a la caracterización de información, identificación de amenazas, ejecución de configuraciones y finalmente validación sobre un escenario de pruebas sobre el dispositivo seleccionado
This study aims to find the best performing model in predicting cryptocurrencies using different machine learning models. In our study, an analysis was performed on various cryptocurrencies such as Aave, BinanceCoin, Bitcoin, Cardano, Cosmos, Dogecoin, Ethereum, Solana, Tether, Tron, USDCoin and XRP. Decision Trees, Random Forests, KNearest Neighbours (KNN), Gradient Boost Machine (GBM), LightGBM, XGBoost, CatBoost, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Short Term Memory networks in Long Comparisons (LSTM) models were used. The performance of the models is compared with Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The study results show that there is no single model that consistently outperforms others for all cryptocurrencies. Models such as XGBoost and Random Forests show consistent and strong performance across different cryptocurrencies, proving their robustness in this particular use case. Deep learning algorithms, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs), show significant accuracy in predicting some cryptocurrencies.
Rihab Qasim Abdulkadhim, Hasanen S. Abdullah, Mustafa Jasim Hadi
Abstract Decentralized cryptocurrencies have received much attention over the last few years. Bitcoin (BTC) has enabled straight online expenditures without the need for centralized financial institutions. Cryptocurrencies are used not only for online payments but are also increasingly used as financial assets. With the rise in the number of cryptocurrencies, including BTC, Ethereum (ETH), and Ripple (XRP), and the millions of daily trades through different exchange services, cryptocurrency trading is prone to challenges similar to those seen in the traditional financial industry, such as price and trend forecasting, volatility forecasting, portfolio building, and fraud detection. This study examines the use of Recurrent neural networks (RNNs) for predicting BTC, ETH, and XRP prices. Accurate price prediction is essential for investors and traders in this volatile market. Machine learning techniques, including RNNs, Long-Short-Term Memory (LSTM), and convolutional neural networks, have been employed to forecast cryptocurrency prices with varying degrees of success. The aim of this study is to evaluate the effectiveness of RNNs in predicting cryptocurrency prices and compare their performance with other established methods. The results indicate that RNNs, particularly LSTMs and Gated Recurrent Units, demonstrate excellent capabilities in accurately predicting currency prices and providing insights to investors and traders in the cryptocurrency market.
Maznun Arifa Mohammadan Makhtar, Novia Admodisastro, Mohd Anuar Mat Isa, Daniel Hafiz Abdullah · 5 authors
People are now widely adopting digital assets in various applications, integrating them into almost every aspect of their lives. Electronic books, or e-books, are one of the digital assets that result from the transformation of physical reading material into the digital world. Nowadays, blockchain is used in many industries because it provides immutable and transparent records. E-book publishers may take this opportunity to adopt blockchain technology for e-book data management. However, blockchain storage is limited; thus, storing the e-book files in blockchain is not recommended. A decentralized storage system, such as InterPlanetary Files Systems (IPFS), is an alternative way to store large files like e-books. IPFS can facilitate the storage of e-book files while the metadata is stored in the blockchain. The e-book metadata should be stored in a structured way for effective search and retrieval. E-book metadata could be added, deleted, and updated occasionally. Nevertheless, some data structures often struggle with dynamic collections of records. This paper proposes a linked list-based smart contract on Ethereum that integrates with IPFS for the e-book management system. We demonstrate the implementation of a linked list smart contract for insertion, deletion, update, retrieval, and traversal of the e-book’s metadata. The result shows that a linked list-based smart contract with IPFS could offer a robust solution for e-book data management. This solution provides more opportunities to explore further security and cryptography approaches toward a secure e-book management system.
The growing dependence on digital financial and banking transactions has brought about a significant focus on implementing strong security protocols. Blockchain technology has proved itself throughout the years to be a reliable solution upon which transactions can safely take place. This study explores the use of blockchain technology, specifically Ethereum Classic (ETC), to enhance the security of digital financial and banking transactions. The aim is to develop a system using an LSTM model to predict and detect anomalies in transaction data. The proposed LSTM model was trained before being tested and the results prove that the proposed model can effectively enhance the security, especially when compared to other studies in the same domain. The proposed model achieved a prediction accuracy of 99.5%, demonstrating its effectiveness in enhancing security by preventing overfitting and identifying potential threats in network activities. The results suggest significant improvements in digital transaction security, enhancing both the traceability and transparency of blockchain transactions while reducing fraud rates. Future work will extend this model's applicability to larger-scale decentralized finance systems.
Abdullah Alshahrani, Ayman E. Khedr, Mohamed Belal, Mohamed Saleh
Transportation management in Egypt faces challenges such as congestion, inefficiency, and a lack of transparency. This work proposes a smart contract-based transportation framework to address these issues and enhance the efficiency of Egypt's transportation system. By leveraging blockchain technology, smart contracts can facilitate and enforce decentralized and immutable transportation agreements. This approach also fosters increased trust among stakeholders and improves interactions between service providers. This paper presents a conceptual framework that integrates smart contracts, blockchain technology, GPS data, and sensor technologies to further optimize transportation operations. Empirical analysis and case studies demonstrate the effectiveness of smart contracts in improving the shipping registration system. The survey results show that smart contracts streamline processes enhance data security, reduce costs, and improve accuracy. The proposed model, developed on the NEAR platform, outperforms traditional methods and Ethereum-based models by offering faster registration, better cost-efficiency, and improved transaction tracking. This demonstrates the potential for modernizing and optimizing Egypt’s transportation sector.
The rapid advancement of grid modernization and the proliferation of smart grids have engendered a critical need for cyber-physical security. Recent cyber-attacks targeting grid infrastructure, notably leading to substantial blackouts in Ukraine, underscore the vulnerabilities and potentially catastrophic consequences of such incursions. These attacks, whether stemming from cyber threats such as Denial of Service (DOS), False Data Injection Attacks (FDIA), or complex cyber-physical manipulations, emphasize the imperative of robust cybersecurity protocols in smart grid operations. This research investigates a pivotal approach to fortify and safeguard smart grid systems by integrating blockchain technology with wireless sensor nodes. By leveraging a Proof of Authority (PoA) Ethereum Blockchain framework, the study delves into the transformative capabilities of Blockchain within Supervisory Control and Data Acquisition (SCADA) networks. Specifically, it examines configurations across IEEE 14-bus, 30-bus, and 118-bus topologies. In addition to elucidating the inherent vulnerabilities in traditional SCADA systems, this study meticulously evaluates an array of performance matrices. Statistical analyses encompassing mean, standard deviation, skewness, kurtosis, and confidence levels provide nuanced insights into the efficacy of blockchain mechanisms in enhancing SCADA resilience against contemporary cyber threats. This research endeavors to bridge the gap in modern cybersecurity paradigms by fusing blockchain technology with wireless sensor nodes. By fortifying data integrity, elevating the reliability of data transmission, and augmenting trustworthiness within SCADA infrastructures, this study aims to present robust solutions to the escalating cybersecurity challenges faced by smart grid systems.
David Alaminos, M. Belén Salas, Ángela Callejón Gil
<abstract> <p>The blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict "buy" or "sell" decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.</p> </abstract>
Elton Farias de Oliveira, David Barbosa de Alencar, Jean Mark Lobo de Oliveira
Este artigo explora o desenvolvimento de smart contracts e tokens na rede Ethereum, destacando os benefícios de automação de acordos, eliminação de intermediários, transparência e segurança proporcionados pela tecnologia blockchain. São discutidos desafios como escalabilidade, privacidade e segurança, e são abordadas as possibilidades de construção de aplicativos descentralizados inovadores. A pesquisa adotou uma abordagem de coleta de dados primários e secundários, incluindo entrevistas com especialistas e análise de contratos inteligentes existentes.
Electronic Toll Collection (ETC) systems have been proposed as a replacement for traditional toll booths, where vehicles are required to queue to make payments, particularly during holiday period. Thus, the primary advantage of ETC is improved traffic efficiency. However, existing ETC systems lack the security necessary to protect vehicle information privacy and prevent fund theft. As a result, automatic payments become inefficient and susceptible to attacks, such as Reentrancy attack. In this paper, we utilize Ethereum blockchain and smart contracts as the automatic payment method. The biggest challenges are to authenticate the vehicle data, automatically deducts fees from the user’s wallet and protects against smart contract Reentrancy Attack without leaking distance information. To address these challenges, we propose an end-to-end Verification algorithms at both entry and exit toll points that corporate measures to protect distance-related information from potential leaks. The proposed system’s performance was evaluated on a private blockchain. Results demonstrate that our approach enhances transaction security and ensures accurate payment processing.
In recent years, particularly with the Ethereum blockchain’s advent, smart contracts have gained significant interest as a means of regulating exchanges among multiple parties via code. This surge has prompted the emergence of various smart contract (SC) programming languages, each possessing distinct philosophies, grammatical structures, and components. Conse-quently, developers are increasingly involved in SC programming. However, these languages are platform specific, implying that a transition to another platform necessitates the use of different languages. Additionally, developers require a certain level of control over SCs to address encountered bugs and ensure maintenance. To address these developer-centric challenges, this paper presents SCEditor, a novel Eclipse Sirius-based prototype editor designed for the visualization, design, and creation of SCs. The editor proposes a means of standardizing the usage of SC programming languages through the incorporation of graphical syntax and a metamodel conforming to Model-Driven Engineering (MDE) principles and SC construction rules to generate an abstract SC model. The efficacy of this editor is demonstrated through testing on a voting SC written in Vyper and Solidity languages. Furthermore, the editor holds potential for future exploitation in model transformation and code generation for various SC languages.
In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings...
Akaki Mamageishvili, Christoph Schlegel, Benny Sudakov, Danning Sui
We study the amount of maximal extractable value (MEV) captured by validators, as a function of searcher competition, in blockchains with competitive block building markets such as Ethereum. We argue that the core is a suitable solution concept in this context that makes robust predictions that are independent of implementation details or specific mechanisms chosen. We characterize how much value validators extract in the core and quantify the surplus share of validators as a function of searcher competition. Searchers can obtain at most the marginal value increase of the winning block relative to the best block that can be built without their bundles. Dually this gives a lower bound on the value extracted by the validator. If arbitrages are easy to find and many searchers find similar bundles, the validator gets paid all value almost surely, while searchers can capture most value if there is little searcher competition per arbitrage. Moreover, mechanisms that implement core allocations in dominant strategies, for submodular values, there is a unique dominant-strategy incentive compatible core-selecting mechanism that gives each searcher exactly their marginal value contribution to the winning block. We extend our model to multiple concurrent proposers in which, under mild assumptions, the core is empty. We validate our theoretical prediction empirically with aggregate bundle data and find a significant positive relation between the number of submitted backruns for the same opportunity and the median value captured by the proposer from the opportunity.
Process (or workflow) execution on blockchain suffers from limited scalability; specifically, costs in the form of transactions fees are a major limitation for employing traditional public blockchain platforms in practice. Research, so far, has mainly focused on exploring first (Bitcoin) and second-generation (e.g., Ethereum) blockchains for business process enactment. However, since then, novel blockchain systems have been introduced - aimed at tackling many of the problems of previous-generation blockchains. We study such a system, Algorand, from a process execution perspective. Algorand promises low transaction fees and fast finality. However, Algorand's cost structure differs greatly from previous generation blockchains, rendering earlier cost models for blockchain-based process execution non-applicable. We discuss and contrast Algorand's novel cost structure with Ethereum's well-known cost model. To study the impact for process execution, we present a compiler for BPMN Choreographies, with an intermediary layer, which can support multi-platform output, and provide a translation to TEAL contracts, the smart contract language of Algorand. We compare the cost of executing processes on Algorand to previous work as well as traditional cloud computing. In short: they allow vast cost benefits. However, we note a multitude of future research challenges that remain in investigating and comparing such results.
A blockchain platform is generally cohabited by human users, autonomous agents, cryptocurrencies, other digital assets, and decentralized protocols. As an example, consider the Ethereum ecosystem - currently the most actively used and the second-largest cryptocurrency network by market capitalization after Bitcoin. Ether is the native cryptocurrency of Ethereum that is transferred between accounts. Ethereum accounts are of two types: Externally owned accounts are controlled by users, whereas a contract account is controlled by a smart contract, which is an autonomous agent and can execute complex code across a decentralized network. For instance, smart contracts can define tokens that are digital assets in the blockchain platform. Decentralized applications (dApps) such as exchanges, wallets, and DeFi may combine multiple smart contracts and their protocols constitute a collection of rules that govern dApps in a decentralized blockchain platform. Complex interactions across various actors in blockchains generate massive-scale, dynamic, heterogeneous, and multi-modal data that are often publicly accessible and can be considered big data – an emerging trend since the past decade. Analysis of blockchain data using the latest data management and AI techniques is critical for the improvement of the blockchain technology, such as detecting and predicting trends, anomalies, e-crimes, and key actors. <br/><br/>In the first part of the talk, I shall discuss our recent work on blockchain data extraction and graph construction, graph mining, topological data analysis, and machine learning methods for various target applications such as detecting market manipulators in the blockchain world including the collapse of the stablecoin LunaTerra, Ethereum’s switch from Proof-of-Work (PoW) to Proof-of-Stake (PoS), and the stablecoin USDC’s temporary peg loss. In the second part, I shall showcase the contributions of blockchain technology in the growing ecosystem of data management and AI – in the form of diverse datasets, tools, novel challenges, and algorithms. I shall conclude by emphasizing future research directions such as cross-chain data analysis, combining signals from external sources, e.g., tweets and social media data about blockchains for holistic predictions, higher-order and multi-modal network analysis, designing of temporal machine learning and machine unlearning algorithms.
Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies-Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)-over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets.