Edgar Roberto Dulce Villarreal, J. Garcia-Alonso, Julio Ariel Hurtado Alegría
Purpose The use of technology applied to the care of ageing adults is a key strategy to increase the effectiveness of health care delivery, thus contributing to a higher quality of life for ageing people.However, a general and important concern is data security and privacy.Given the security provided by Blockchain (BC), the interest in this technology is growing at an accelerated pace in different contexts, and support for aging is no exception.Security in this type of technologies rests, among other things, on Smart Contracts (SC), immutable decentralized programs for BC platforms that enforce, monitor and execute agreements, without the intervention of a trusted third party.Due to the variety of technologies and their particularities, the development of SCs is a complex process, since the architectural constraints of each platform must be considered.This paper presents a Model Driven Engineering (MDE) tool that automatically generates SCs using the Solidity programming language, in the context of a senior care process, for deployment on the Ethereum platform.This tool complements and serves as a starting point for the framework presented in (E.R. D. Villarreal, et al. 2023).For this purpose, an Ethereum SEPM Platform Specific Metamodel and a Model to Text Transformation to generate the SCs (SEP2Solidity) are presented (See the additional material in a public repository 1 ).As a proof of concept, a metamodel, a model and a deployed SC was generated and implemented, using the functionality assessment on the Elderly Nursing Core Set (ENCS) (M, Lopes.2013).The ENCS assesses quality of life in terms of functioning among ageing adults (based on the International Classification of Functioning, Disability and Health (ICF)).Method Our aim to contribute to the care of ageing people began with the identification of a problem (Dulce, E., Hurtado, J. 2021).We have analyzed the contributions that BC can have in the care of ageing people and identified shortcomings of BC technology, directly in the development of SC.In the literature review, we have analyzed the significant contributions that MDE technology can have to perform the specification and transformation of SC between different BC platforms.Based on MDE, we have created a 4-level architecture, where we have defined the real-world elements (M0), the models (M1) and the meta-models (M2) required for the whole MDE ecosystem (Figure 1).For the construction of the metamodel, we followed the interactive and iterative approach proposed in (N.Sanchez, 2022) this, allows the specification of model fragments by domain experts.These fragments can be annotated with descriptions about the intent or requirements of certain elements.A metamodel is automatically induced, which can be interactively refactored and then compiled into an implementation metamodel for different platforms and purposes.In our case for the Ethereum BC platform.The Eclipse Modelling Framework development environment was used (eCore as metamodel, Acceleo for m2t transformation) and also, Remix IDE was used for SC deployment.Results and Discussion With the metamodel created, the model of an SC for the ENCS administration was created, then, with the m2t transformation, the source code of the SC was generated.Remix was then used to implement and deploy the SC (deploy.docxfile in the supplementary material).The data obtained in Remix shows the successful deployment of the SC.Furthermore, the results indicate that our metamodel is able to generate: constructors, users, assets, global and local variables, primitive data types, functions, mappings, structures, events, among others, required in SCs for Ethereum BC platforms, preserving the syntax of the solidity.Likewise, our tool can contribute in the maturation and specification of SCs that support ageing care, directly in secure and privacy data management, e.g., ENCS, since ultimately the management of their data depends on well-structured SCs.Also, moving forward in our work, we will create this same scenario for other BC platforms and programming languages.This will enhance SC transformation between different BC platforms, contributing to the interoperability and security of this entire ecosystem.
Genetika manusia merujuk pada informasi yang dikumpulkan tentang genom atau warisan genetik individu manusia. Data ini mencakup sekuens DNA, variasi genetik, mutasi, dan informasi lain yang terkait dengan sifat dan karakteristik genetik individu manusia. Data genetika manusia diperoleh melalui serangkaian proses, meliputi penguntaian genetik, pengujian genetik, analisis DNA, dan pemetaan genetik. Data genetika terutama pada manusia merupakan data yang bersifat privat yang harus dilindungi keamanan dan kerahasiaanya. Beberapa penelitian telah menggunakan teknologi Blockchain untuk menyimpan data yang memerlukan keamanan ekstra. Blockchain memberikan solusi untuk perlindungan dan pengelolaan data dengan fitur teknologinya yang terdesentralisasi, terenkripsi, setiap transaksi bisa ditelusuri, dan antitampering atau sulit dimodifikasi. Penelitian menerapkan teknologi Blockchain untuk menyimpan dan mengelola data genetik. Sebagai bahan penelitian data genetika manusia diakusisi dari NCBI repository. Data genetik tersebut disimpan dalam Smart contract pada blockchain Ethereum yang ditulis menggunakan bahasa pemrograman Solidity. Setiap transaksi dan penyimpanan data pada Ethereum dibebankan biaya yang cukup mahal atau yang dikenal dengan biaya gas maka penelitian ini menawarkan solusi hanya menyimpan signature saja dari data genetik itu dalam blockchain. Data genetik yang riil dan berukuran besar disimpan dalam InterPlanetary File System (IPFS). Hasil pengujian menjalankan smart contract pada blockchain Ethereum yang hanya menyimpan signature data genetik ini menunjukkan biaya gas yang sangat efisien karena hanya menyimpan 256 bit saja dari data genetik riilnya yang dapat mencapai giga byte. Abstract Human genetics refers to information gathered about the genome or genetic heritage of human individuals. This data includes DNA sequences, genetic variations, mutations, and other information related to individual human genetic traits and characteristics. Human genetic data is obtained through a series of processes, including genetic sequencing, genetic testing, DNA analysis, and genetic mapping. Genetic data, especially in humans, is private data that must be protected by security and confidentiality. Several studies have used Blockchain technology to store data that requires extra security. Blockchain provides solutions for data protection and management with its technological features that are decentralized, encrypted, every transaction can be traced, and anti-tampering or difficult to modify. Research uses Blockchain technology to store and manage genetic data. As research material, human genetic data was acquired from the NCBI repository. The genetic data is stored in Smart contracts on the Ethereum blockchain written using the Solidity programming language. Every transaction and data storage on Ethereum is charged with a fairly expensive fee, known as a gas fee, so this research offers a solution by only storing the signature of the genetic data in the blockchain. The real and large-scale genetic data is stored in the InterPlanetary File System (IPFS). The test results of running a smart contract on the Ethereum blockchain that only stores genetic data signatures show a very efficient gas cost because it only stores 256 bits of real genetic data, which can reach gigabytes.
Rodrigo Craveiro Rodrigues, Pedro Miguel Calhau Mateus, Valderi Reis Quietinho Leithardt
With the advancement of cloud computing, data storage, and security have become crucial. The growing adoption of cloud services by companies, accompanied by increased threats from cybersecurity, highlights the importance of privacy and ownership of user data. Between 2022 and 2023, there has been an increase of around 48% in cloud security threats, emphasizing the urgent need for strong security solutions. To face these challenges, in this project, we propose integrating the Ethereum network's blockchain technology with a database located in the PostgreSQL cloud. The proposed solution aims to provide bidirectional data synchronization and strict control of access mechanisms. Blockchain technology ensures immutability and transparency of transactions, while PostgreSQL provides efficient and scalable storage. Through rigorous testing in an adaptive traffic control scenario, the results obtained indicate that this solution offers a significantly high level of security due to the decentralization of data, confirming that this solution is effective, and making it a powerful new option to improve security in cloud environments. In conclusion, the solution proposed in this project not only increases information security but also demonstrates the practical feasibility of integrating blockchain with cloud relational databases. This two-way alignment improves protection against cyberattacks and ensures that user data is protected from unauthorized access and malicious changes.
Ole Delzer, Richard Hobeck, Ingo Weber, Dominik Kaaser · 6 authors
Abstract The growing popularity of blockchains highlights the need to improve their scalability. While previous research has focused on scaling transaction processing, the scalability of transaction creation remains unexplored. This issue is particularly important for organizations needing to send large volumes of transactions quickly or continuously. Scaling transaction creation is challenging, especially for blockchain platforms like Ethereum, which require transactions to include a sequence number. This paper proposes four different methods to scale transaction creation. Our experimental evaluation assesses the scalability and latency of these methods, identifying two as feasible for scaling transaction creation. Additionally, we provide an in-depth theoretical analysis of these two methods.
Tokens have proliferated across blockchains in terms of number, market capitalisation and utility. Some tokens are tokenised versions of existing tokens -- known variously as wrapped tokens, fractional tokens, or shares. The repeated application of this process creates matryoshkian tokens of arbitrary depth. We perform an empirical analysis of token composition on the Ethereum blockchain. We introduce a graph that represents the tokenisation of tokens by other tokens, and we show that the graph contains non-trivial topological structure. We relate properties of the graph, e.g., connected components and cyclic structure, to the tokenisation process. For example, we identify the longest directed path and its corresponding sequence of tokens, and we visualise the connected components relating to a stablecoin and an NFT protocol. Our goal is to explore and visualise what has been wrought with tokens, rather than add yet another brick to the edifice.
Validators in permissionless, large-scale blockchains, such as Ethereum, are typically payoff-maximizing, rational actors. Ethereum relies on in-protocol incentives, like rewards for correct and timely votes, to induce honest behavior and secure the blockchain. However, external incentives, such as the block proposer's opportunity to capture maximal extractable value (MEV), may tempt validators to deviate from honest protocol participation. We show a series of commitment attacks on LMD GHOST, a core part of Ethereum's consensus mechanism. We demonstrate how a single adversarial block proposer can orchestrate long-range chain reorganizations by manipulating Ethereum's reward system for timely votes. These attacks disrupt the intended balance of power between proposers and voters: by leveraging credible threats, the adversarial proposer can coerce voters from previous slots into supporting blocks that conflict with the honest chain, enabling a chain reorganization. In response, we introduce a novel reward mechanism that restores the voters' role as a check against proposer power. Our proposed mitigation is fairer and more decentralized, not only in the context of these attacks, but also practical for implementation in Ethereum.
Maximal Extractable Value (MEV) represents a pivotal challenge within the Ethereum ecosystem; it impacts the fairness, security, and efficiency of both Layer 1 (L1) and Layer 2 (L2) networks. MEV arises when miners or validators manipulate transaction ordering to extract additional value, often at the expense of other network participants. This not only affects user experience by introducing unpredictability and potential financial losses but also threatens the underlying principles of decentralization and trust. Given the growing complexity of blockchain applications, particularly with the increase of Decentralized Finance (DeFi) protocols, addressing MEV is crucial. This paper presents a comprehensive survey of MEV mitigation techniques as applied to both Ethereums L1 and various L2 solutions. We provide a novel categorization of mitigation strategies; we also describe the challenges, ranging from transaction sequencing and cryptographic methods to reconfiguring decentralized applications (DApps) to reduce front-running opportunities. We investigate their effectiveness, implementation challenges, and impact on network performance. By synthesizing current research, real-world applications, and emerging trends, this paper aims to provide a detailed roadmap for researchers, developers, and policymakers to understand and combat MEV in an evolving blockchain landscape.
This research paper investigates how Ethereum blockchain technology can transform real estate investment through crowdfunding. Traditional real estate markets often encounter issues related to liquidity, accessibility, and transparency; this study addresses these challenges by proposing a blockchain-based solution. It examines the use of Ethereum smart contracts, Initial Coin Offerings (ICOs), and ERC20 tokens to facilitate fractional ownership of properties and automate investment processes. The technical stack for the proposed system includes Vue.js for the front-end, Solidity for developing smart contracts, and the Truffle framework for deployment. Ethereum is used as the blockchain platform, with Ganache-CLI providing a local testing environment. MetaMask is integrated for managing digital wallets, and Stripe is used for processing fiat currency, showcasing a robust approach to blending blockchain technology with current financial systems. This paper offers a detailed examination of the practical applications and challenges of using blockchain in real estate crowdfunding. The findings indicate that while this technology holds great potential for resolving persistent issues in real estate investment, important factors such as scalability, regulatory compliance, and user adoption still need to be carefully considered [4]. Keywords: Blockchain, Ethereum, Smart Contracts, Real Estate Crowdfunding, Tokenization, ERC-20 Tokens, ICO, Fractional Property Ownership, Liquidity, Transparency, Ganache, Remix IDE, MetaMask, Solidity, Truffle Framework.ramework.
Smart contracts are central to a myriad of critical blockchain applications, from financial transactions to supply chain management. However, their adoption is hindered by security vulnerabilities that can result in significant financial losses. Most vulnerability detection tools and methods available nowadays leverage either static analysis methods or machine learning. Unfortunately, as valuable as they are, both approaches suffer from limitations that make them only partially effective. In this survey, we analyze the state of the art in machine-learning vulnerability detection for Ethereum smart contracts, by categorizing existing tools and methodologies, evaluating them, and highlighting their limitations. Our critical assessment unveils issues such as restricted vulnerability coverage and dataset construction flaws, providing us with new metrics to overcome the difficulties that restrain a sound comparison of existing solutions. Driven by our findings, we discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts. Our guidelines address the known flaws while at the same time opening new avenues for research and development. By shedding light on current challenges and offering novel directions for improvement, we contribute to the advancement of secure smart contract development and blockchain technology as a whole.
In the context of the increasing importance of digitalization and cybersecurity, exploring the application of blockchain and smart contract technologies in blood donation management aims to enhance data transparency and privacy protection, addressing challenges in current healthcare systems. This paper explores the transformative potential of blockchain and smart contract technologies in digitalizing the blood donation process. By leveraging the Ethereum platform and employing a Browser/Server (B/S) architecture integrated with Solidity programming and web development practices, this paper proposes a novel framework designed to enhance the transparency, digitalization, and shared accessibility of blood donation data. The implementation of this system within a healthcare context promises to streamline the donation process, ensuring the integrity and confidentiality of donor data, thereby fostering trust among all stakeholders involved. The findings indicate that the application of blockchain technology not only facilitates a more efficient and secure management of blood donation records but also sets a precedent for future healthcare innovations.
Blockchain is a promising infrastructure for the internet and digital economy, but it has serious scalability problems, that is, long block synchronization time and high storage cost. Conventional coarse-grained data deduplication schemes (block or file level) are proved to be ineffective on improving the scalability of blockchains. Based on comprehensive analysis on typical blockchain workloads, we propose two new locality concepts (economic and argument locality) and a novel fine-grained data deduplication scheme (transaction level) named Alias-Chain. Specifically, Alias-Chain replaces frequently used data, for example, smart contract arguments, with much shorter aliases to reduce the block sizes, which results in both shorter synchronization time and lower storage cost. Furthermore, to solve the potential consistency issue in Alias-Chain, we propose two complementary techniques: one is generating aliases from history blocks with high consistency, and the other is speeding up the generation of aliases via a specific algorithm. Our simulation results show: (1) the average transfer and SC-call transaction (a transaction used to call the smart contracts in the blockchain) sizes can be significantly reduced by up to 11.03% and 79.44% in native Ethereum, and up to 39.29% and 81.84% in Ethereum optimized by state-of-the-art techniques; and (2) the two complementary techniques well address the inconsistency risk with very limited impact on the benefit of Alias-Chain. Prototyping-based experiments are further conducted on a testbed consisting of up to 3200 miners. The results demonstrate the effectiveness and efficiency of Alias-Chain on reducing block synchronization time and storage cost under typical real-world workloads.
The evolution of authentication mechanisms in ensuring secure access to systems has been crucial for mitigating vulnerabilities and enhancing system security. However, despite advancements in two-factor authentication (2FA) and multi-factor authentication (MFA), authentication mechanisms remain weak in system security, particularly when individuals accessing critical systems are involved. In response to this challenge, we propose a novel blockchain-based multi-factor dynamic authentication mechanism (BMFA) that integrates honeytoken technology to enhance security. Our proposed mechanism leverages Ethereum blockchain technology and smart contracts to provide a decentralized and robust authentication framework. By incorporating honeytokens into smart contracts, we introduce a dynamic layer of security that continuously adapts to prevent potential attacks. Our evaluation demonstrates that our BMFA mechanism effectively addresses various security challenges, including brute force attacks, man-in-the-middle attacks, and smart contract vulnerabilities, while providing robust protection against unauthorized access. Our findings emphasise the efficacy of the BMFA mechanism in enhancing system security and mitigating evolving threats in authentication processes for next-generation critical industrial control systems.
Open access
Blockchain Technology Applications and Security
Currency Recognition and Detection
Advanced Steganography and Watermarking Techniques
One of the financial assets in currency exchange is now cryptocurrency. The public is drawn to cryptocurrency trading because it is considered a lucrative form of investing. For cryptocurrency investors to maximize their earnings, accurate price forecasting is crucial. As price forecasting involves time series analysis, a hybrid deep learning model is suggested to project cryptocurrency prices in the future. Long Short-Term Memory and Gated Recurrent Unit (LSTM-GRU) networks are integrated into the hybrid model. Three cryptocurrency datasets are evaluated using the suggested hybrid model: Ethereum, Ripple, and Bitcoin. According to experimental results, the suggested LSTM-GRU model may provide the lowest MSE and RMSE values on the Bitcoin dataset (0.0611 and 0.2472), the Ethereum dataset (0.0369 and 0.19222), and the Ripple dataset (0.0006 and 0.0247).
This paper presents an in-depth exploration of Data Availability Sampling (DAS) and sharding mechanisms within decentralized systems through simulation-based analysis. DAS, a pivotal concept in blockchain technology and decentralized networks, is thoroughly examined to unravel its intricacies and assess its impact on system performance. Through the development of a simulator tailored explicitly for DAS, we embark on a comprehensive investigation into the parameters that influence system behavior and efficiency. A series of experiments are conducted within the simulated environment to validate theoretical formulations and dissect the interplay of DAS parameters. This includes an exploration of approaches such as custody by row, variations in validators per node, and malicious nodes. The outcomes of these experiments furnish insights into the efficacy of DAS protocols and pave the way for the formulation of optimization strategies geared towards enhancing decentralized network performance. Moreover, the findings serve as guidelines for future research endeavors, offering a nuanced understanding of the complexities inherent in decentralized systems. This study not only contributes to the theoretical understanding of DAS but also offers practical implications for the design, implementation, and optimization of decentralized systems.
This study aims to analyze the impact of internal variables, including total Ethereum, number of transactions, fees per transaction, and number of active wallets, as well as external variables, namely the price of Bitcoin and the price of gold, on global Ethereum prices. The study utilizes daily data covering the period from December 31, 2016, to December 31, 2021. The data analysis employs time series data with the assistance of Eviews 10 and the error correction model (ECM) method. The study's findings indicate that total Ethereum, number of transactions, fees per transaction, number of active wallets, price of Bitcoin, and price of gold collectively exert a significant influence on Ethereum prices. However, when examined individually, total Ethereum demonstrates a negative impact and lacks statistical significance on Ethereum prices. Similarly, the number of transactions exhibits a negative and significant effect on Ethereum prices. Conversely, transaction fees, number of active wallets, and the price of Bitcoin have a positive and significant impact on Ethereum prices. Meanwhile, global gold prices do not exhibit any influence on Ethereum prices.
Vijay Jayadev, Naghmeh Moradpoor, Andrei Petrovski
Blockchain technology offers a decentralized and secure platform for addressing various challenges in smart cities and cyber-physical systems, including identity management, trust and transparency, and supply chain management. However, blockchains are susceptible to a variety of threats, akin to any other technological system. To assess the resilience and robustness of diverse blockchain technologies, this study evaluates their performance indicators under various attack scenarios. Therefore, this study conducts a thorough examination of multiple well-known blockchain technologies, such as Ethereum and Hyperledger Fabric, under Distributed Denial of Service attack scenarios. Ethereum, introduced as a revolutionary blockchain technology, has entirely transformed the way smart contracts and decentralized applications operate. Additionally, the innovative open source blockchain framework, Hyperledger Fabric, is intended for businesses and alliances seeking a secure and adaptable platform to develop distributed ledger applications. Hyperledger Besu, an Ethereum client with an extractable Ethereum Virtual Machine implementation designed to be enterprise-friendly for both public and private permissioned network use cases. Therefore, Ethereum and Hyperledger Fabric are utilized in this study for performance comparison. This study provides a summary of Ethereum's salient characteristics, architecture, and noteworthy influence on the blockchain and cryptocurrency ecosystem. Furthermore, it offers an overview of the main characteristics, architecture, and potential uses of Hyperledger Fabric. The blockchain's resilience against DDoS attacks is assessed by examining performance measures such as latency and throughput, which are fundamental metrics crucial for evaluating and enhancing the effectiveness of various systems, including communication protocols, databases, blockchains, and computer networks. The outcomes of these experiments show that Hyperledger Fabric has greater throughput and reduced latency, demonstrating its resistance to DDoS attacks in comparison with Ethereum. Ethereum, being a permissionless blockchain, can introduce challenges such as the potential for network congestion and scalability issues.
Al Amin, Rushabh Shah, Hemanth Tummala, Indrajit Ray
Healthcare insurance provides financial support to access medical services for patients while ensuring timely and guaranteed payment for providers. Insurance fraud poses a significant challenge to insurance companies and policyholders, leading to increased costs and compromised healthcare treatment and service delivery. Most frauds, like phantom billing, upcoding, and unbundling, happen due to the lack of required entity participation. Also, claim activities are not transparent and accountable. Fraud can be prevented and minimized by involving every entity and making actions transparent and accountable. This paper proposes a blockchain-powered smart contract-based insurance claim processing mechanism to prevent and minimize fraud in response to this prevailing issue. All entities-patients, providers, and insurance companies-actively participate in the claim submission, approval, and acknowledgment process through a multi-signature technique. Also, every activity is captured and recorded in the blockchain using smart contracts to make every action transparent and accountable so that no entity can deny its actions and responsibilities. Blockchains' immutable storage property and strong integrity guarantee that recorded activities are not modified. As healthcare systems and insurance companies continue to deal with fraud challenges, this proposed approach holds the potential to significantly reduce fraudulent activities, ultimately benefiting both insurers and policyholders. The average gas costs for smart contract deployment, claim submission, and multi-signature for the Ethereum network: $80.22, $20.60, and $6.47, and for the Optimism network: $0.35, $0.089, and $0.028. They are feasible for the proposed approach.
Luca Aceto, Daniele Gorla, Stian Lybech, Mohammad Hamdaqa
We continue the development of TinySol, a minimal object-oriented language based on Solidity, the standard smart-contract language used for the Ethereum platform. We first extend TinySol with exceptions and a gas mechanism, and equip it with a small-step operational semantics. Introducing the gas mechanism is fundamental for modelling real-life smart contracts in TinySol, since this is the way in which termination of Ethereum smart contracts is usually ensured. We then devise a type system for smart contracts guaranteeing that such programs never run out of gas at runtime. This is a desirable property for smart contracts, since a transaction that runs out of gas is aborted, but the price paid to run the code is not returned to the invoker.
Recent estimates put the carbon footprint of Bitcoin and Ethereum at an average of 64 and 26 million tonnes of CO2 per year, respectively. To address this growing problem, several possible approaches have been proposed in the literature: creating alternative blockchain consensus mechanisms, applying redundancy reduction techniques, utilizing renewable energy sources, and employing energy-efficient devices, etc. In this paper, we follow the second avenue and propose an efficient approach based on reinforcement learning that improves the block broadcasting scheme in blockchain networks. The analysis and experimental results confirmed that the proposed improvement of the block propagation scheme could cleverly handle network dynamics and achieve better results than the default approach. Additionally, our technical integration of the simulator and developed RL environment can be used as a complete solution for further study of new schemes and protocols that use RL or other ML techniques.
Runtime Verification (RV) of smart contracts ensures the absence of exploitations within a transaction during execution. It is a crucial security aspect that is often omitted due to its high onchain overhead. The lack of RV in public blockchains allowed attackers to compromise vulnerable contracts and cause significant monetary losses. Existing runtime protection solutions fail to address the onchain overhead limitation, which may hinder their deployment and undermine their effectiveness. To address this problem, we propose SRP (Smart contract Runtime Protection), an efficient framework that integrates an off-chain mechanism with on-chain contract execution to minimize the burden of RV. Our hybrid architecture aims to protect deployed smart contracts from real-time attacks while maintaining blockchain scalability. We first present SRP from a design perspective proposing a protocol customized for off-chain RV interoperability. Then, we evaluate our approach empirically and demonstrate the applicability of SRP using a proof-of-concept implementation on a local Ethereum network. We further generalize our evaluation results using a queue-theoretic approach. We present a discrete-time queuing network model of SRP and the block creation process. The model is then used to conduct an extensive data-driven parametric evaluation of SRP compared to onchain-only RV. Our empirical, experimental, and quantitative results indicate the feasibility and efficiency of our approach, where SRP outperforms the onchain-only mechanism in terms of service time and throughput, for increasing workloads. Lastly, we introduce a game-theoretic incentive mechanism enhancing the robustness and security of SRP. We analyze the security of SRP and demonstrate that the presence of at least one honest validator serves as a deterrent to RV forging attacks.
Hassan Khalid, Saeed Moradi, Shaiful Chowdhury, Sara Rouhani
The rise of decentralized applications (dApps) has made smart contracts imperative components of blockchain technology. As many smart contracts process financial transactions, their security is paramount. Moreover, the immutability of blockchains makes vulnerabilities in smart contracts particularly challenging because it requires deploying a new version of the contract at a different address, incurring substantial fees paid in Ether. This paper proposes Ethstractor, the first smart contract collection tool for gathering a dataset of versioned smart contracts. The collected dataset is then used to evaluate the reliability of code metrics as indicators of vulnerabilities in smart contracts. Our findings indicate that code metrics are ineffective in signalling the presence of vulnerabilities. Furthermore, we investigate whether vulnerabilities in newer versions of smart contracts are mitigated and identify that the number of vulner-abilities remains consistent over time. Finally, we examine the removal of self-admitted technical debt in contracts and uncover that most of the introduced debt has never been subsequently removed.
Given that cryptocurrencies are now involved in nearly every financial transaction due to their widespread acceptance as an alternative method of payment and currency exchange, researchers and economists have increased opportunities to analyze cryptocurrency prices. Over time, predicting the daily closing price of Ethereum has been challenging for investors, traders, and investment banks because of its significant price volatility. The daily closing price of cryptocurrency is crucial for trading or investing in Ethereum. This report aims to conduct a comparative analysis of the predictive performance of deep machine learning algorithms within a stacking ensemble modeling framework, utilizing daily historical price data of Ethereum from Coindesk, tweets from Twitter spanning from August 1, 2022, to August 8, 2022, and five additional covariates (closing price lag1, closing price lag2, noltrend, daytype, and month) derived from Ethereum's closing price. Seven models are employed to forecast the daily closing price of Ethereum: recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks, and a stacked ensemble of gradient boosting machine, generalized linear model, distributed random forest, and deep neural networks. The primary evaluation metric is the mean absolute error (MAE). Based on MAE, the RNN forecasts outperform the other models in this study, achieving an MAE of 0.0309.
As of December 2021, the cryptocurrency market had a market value of over US$270 billion, and over 5,700 types of cryptocurrencies were circulating among 23,000 online exchanges. Reinforcement learning (RL) has been used to identify the optimal trading strategy. However, most RL-based optimal trading strategies adopted in the cryptocurrency market focus on trading one type of cryptocurrency, whereas most traders in the cryptocurrency market often trade multiple cryptocurrencies. Therefore, the present study proposes a method based on deep Q-learning for identifying the optimal trading strategy for multiple cryptocurrencies. The proposed method uses the same training data to train multiple agents repeatedly so that each agent has accumulated learning experiences to improve its prediction of the future market trend and to determine the optimal action. The empirical results obtained with the proposed method are described in the following text. For Ethereum, VeChain, and Ripple, which were considered to have an uptrend, a horizontal trend, and a downtrend, respectively, the annualized rates of return were 725.48%, −14.95%, and − 3.70%, respectively. Regardless of the cryptocurrency market trend, a higher annualized rate of return was achieved when using the proposed method than when using the buy-and-hold strategy.
Blockchain technology is essential for the digital economy and metaverse, supporting applications from decentralized finance to virtual assets. However, its potential is constrained by the "Blockchain Trilemma," which necessitates balancing decentralization, security, and scalability. This study evaluates and compares two leading proof-of-stake (PoS) systems, Algorand and Ethereum 2.0, against these critical metrics. Our research interprets existing indices to measure decentralization, evaluates scalability through transactional data, and assesses security by identifying potential vulnerabilities. Utilizing real-world data, we analyze each platform’s strategies in a structured manner to understand their effectiveness in addressing trilemma challenges. The findings highlight each platform’s strengths and propose general methodologies for evaluating key blockchain characteristics applicable to other systems. This research advances the understanding of blockchain technologies and their implications for the future digital economy. Data and code are available on GitHub as open source.