Gabriel Kabanda, Colletor Tendeukai Chipfumbu, Tinashe Chingoriw
The purpose of the research is to explore and develop Deep Reinforcement Learning and Q-Learning algorithms in order to improve Ethereum cybersecurity in contract vulnerabilities, the smart contract market and research leadership in the area. Deep Reinforcement Learning (Deep RL) is gaining popularity among AI researchers due to its ability to handle complex, dynamic, and particularly high-dimensional cyber protection problems. The benchmark of RL is goal-oriented behavior that increases rewards and decreases penalties or losses, and enhances real-time interaction between an agent and its surroundings. The research paper examines the three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber-attacks.The Design Science Research Paradigm as applied in Information Systems research was used in this research, as it is hinged on the idea that information and understanding of a design problem and its solution are attained in the crafting of an artefact. The proposed constructs were in the form of Deep Reinforcement Learning and Q-Learning algorithms designed to improve Ethereum cybersecurity. Smart contracts on the Ethereum blockchain can automatically enforce contracts made between two unknown parties. Blockchain (BC) and artificial intelligence (AI) are used together to strengthen one another's skills and complement one another. Consensus algorithms (CAs) of BC and deep reinforcement learning (DRL) in ETS were thoroughly reviewed. In order to integrate many DCRs and provide grid services, this article suggests an effective incentive-based autonomous DCR control and management framework. This framework simultaneously adjusts the grid's active power with accuracy, optimizes DCR allocations, and increases profits for all prosumers and system operators. The best incentives in a continuous action space to persuade prosumers to reduce their energy consumption were found using a model-free deep deterministic policy gradient-based strategy. Extensive experimental experiments were carried out utilizing real-world data to show the framework's efficacy.
In recent years, with the rapid development of the digital economy, digital currencies such as Bitcoin and Ethereum have become increasingly popular among the public. Tracking and regulating digital currency transactions have become a challenging technology for the healthy development of the digital economy. That is because blockchain and peer-to-peer networks are the underlying technologies of digital currencies. Blockchain transaction has some new features, such as stronger anonymity and distributed storage. Therefore, it is difficult for the regulatory system to track the transaction relationships among users. Recent studies have shown that the accuracy, time, and space costs of transaction tracking, as well as the trade-offs between them, still need to be improved. In this article, we propose a new blockchain transaction tracking model called BT2(Bitcoin Transaction Tracking Model). BT2first combines an improved sampling aggregation algorithm with a graph neural network. And then, it exploits an inductive aggregation method to effectively generate rich node embeddings for a small number of unobserved nodes. Next, the node embedding vectors are used to generate edge information among nodes through message-passing functions. Finally, we can use the edge information to obtain the relations among Bitcoin accounts. This paper evaluates the model on the real-world dataset and explores the impact of various parameters, such as network depth and iteration time, etc. From the experimental results, the model’s average AUC (area under the ROC curve) can reach up to 0.93, and the average accuracy is 86%. The numerical results indicate that the performance of BT2is better than the state of art methods.
Lioba Heimbach, Eric Schertenleib, Roger Wattenhofer
Lending protocols in decentralized finance enable the permissionless exchange of capital from lenders to borrowers without relying on a trusted third party for clearing or market-making. Interest rates are purely set by the supply and demand of capital according to a pre-defined function. In the lead-up to The Merge: Ethereum blockchain's transition from proof-of-work (PoW) to proof-of-stake (PoS), a fraction of the Ethereum ecosystem announced plans of continuing with a PoW-chain. Owners of ETH - whether their ETH was borrowed or not - would hold the native tokens on each chain. This development alarmed lending protocols. They feared spiking ETH borrowing rates would lead to mass liquidations which could undermine their viability. Thus, the decentralized autonomous organization running the protocols saw no alternative to intervention - restricting users' ability to borrow. We investigate the effects of the merge and the aforementioned intervention on the two biggest lending protocols on Ethereum: AAVE and Compound. Our analysis finds that borrowing rates were extremely volatile, jumping by two orders of magnitude, and borrowing at times reached 100% of the available funds. Despite this, no spike in mass liquidations or irretrievable loans materialized. Further, we are the first to quantify and analyze hard-fork-arbitrage, profiting from holding debt in the native blockchain token during a hard fork. We find that arbitrageurs made in excess of 13 Mio US$, money that was effectively extracted from the platforms' lenders. Finally, we identify alarming security risks to the entire Ethereum ecosystem. Around one-fifth of the staked ETH through LIDO (stETH) was locked as collateral on lending protocols. Mass liquidations caused by spiking rates would have devastating effects on the stETH price and pose security concerns for the consensus layer, as staking power could be bought at a significant discount.
With the rapid growth of the blockchain market, privacy and security issues for digital assets are becoming more important. In the most widely used public blockchains, such as Bitcoin and Ethereum, all activities on user accounts are publicly disclosed, which violates privacy regulations such as EU GDPR. Encryption of accounts and transactions may protect privacy, but it also raises issues of validity and transparency. While encrypted information can protect privacy, it cannot alone verify the validity of a transaction. Additionally, encryption makes it difficult to meet anti-money laundering regulations, such as auditability. In this paper, we propose${\sf Azeroth}$, an auditable zero-knowledge transfer framework.${\sf Azeroth}$connects a zero-knowledge proof to an encrypted transaction, enabling it to check its validation while protecting its privacy.${\sf Azeroth}$also allows authorized auditors to audit transactions.${\sf Azeroth}$is designed as a smart contract for flexible deployment on existing blockchains. We implement the${\sf Azeroth}$smart contract, and execute it on various platforms including an Ethereum testnet blockchain, and measure the time to show the practicality of our proposal. The end-to-end latency of a privacy-preserving transfer takes about$4.4s$. In particular, the client’s transaction generation time with a proof only takes about$0.9s$. The security of${\sf Azeroth}$is proven under the cryptographic assumptions.
Feifei Guo, Guohua Shen, Zhiqiu Huang, Yang Yang · 6 authors
With the advent of IoT technology, the dynamic nature of IoT devices has introduced new obstacles to access control. It is essential to consider the security requirements of the actual physical environment, rendering the traditional access control approach centered on the information space. In the IoT ecosystem, there are several issues such as the dynamics of devices frequently entering and leaving, the lack of computing and storage capacity, and distributed deployment. To address these challenges, this paper proposes the Domain Attribute Based Access Control(DABAC) that incorporates domain elements to implement the physical location limitation of dynamic devices. Moreover, an intelligent gateway is utilized to divide the physical area and act as a proxy to achieve regional device management, automatic networking of devices in the domain, and the dynamic expansion of the sensor network resulting from device entry or exit. Then, given the distributed deployment of devices, smart contracts are employed to deploy access control mechanisms and construct a trusted environment to mitigate threats such as single points of failure. Finally, the DABAC is implemented on the Ethereum platform, simulating a smart medical situation. The experimental results demonstrate that the proposed solution effectively addresses the problem of access control of device dynamics in an untrusted IoT environment while maintaining system security.
Our proposed method utilizes blockchain technology to safeguard the ownership of specific regions within image data. In our approach, diverse values could be assigned to each region based on its importance, and only users with ownership rights can access these designated regions. This ensures the protection of ownership rights for individuals in any given region of an image. Identified regions are individually encrypted using an XOR cipher, and a corresponding key image is generated for decryption, thereby preserving the privacy of the encrypted region. Non-fungible tokens (NFTs) are employed to protect the key image and manage the ownership of each object in the image data. The NFT for the key image is generated by the key holder (who possesses the entire image), and the ownership NFT is acquired by the user who needs access to the key NFT. Furthermore, the ownership NFT and the key NFT are verified for a match by the judgment function, and only upon successful validation, the NFT is displayed on the screen. This method enables different values to be assigned to various parts of an image, facilitating the transfer and sharing of ownership. Additionally, the original image?s owner can benefit financially based on the value of the image, thus enhancing the overall security of image data.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The broad usage of images in real-time applications demands a cloud infrastructure due to its advantages. Many use cases are built where the image data is shared, sharing becomes the core function, and the medical domain takes its broad advantage. The cloud is a centralized infrastructure for its all-operation usages; it depends mainly on the trusted third party to handle security concerns. Therefore, the privacy preservation of the image data or any data becomes an issue of concern. The distrusted system advantages are achieved using blockchain technology for image data security and privacy concerns. The traditional approaches of the security and privacy models raise many apprehensions as these are designed on the centralized systems of the data sharing mechanisms. It is also observed that large data files are not wisely handled, which demands building a framework model that takes image data and any other data of any size to ensure a dependable optimal security system. This paper presents a framework model to achieve optimal time complexity for securing the privacy aspects of the image data or any other data that uses space optimal file system using distributed security mechanism for both the storage and sharing of the data. The proposed framework model for optimal time complexity and security uses a duplication algorithm using stakeholder agreement to ensure efficient access control to the resources using the cryptographic approach to the Ethereum ecosystem. The performance metric used in the model evaluation includes the degree of availability and efficiency. On benchmarks, it performs well compared to the traditional cloud-built distributed systems. The quantified outcome of the proposed scheme exhibits a 42.5% of reduction in time for data repositioning, a 41.1% of reduction in time for data retrieval, a 34.8% of reduction in operational cost, a 73.9% of reduction in delay, and a 61% faster algorithm execution time in contrast to conventional blockchain method.
The successful operation of a blockchain system crucially relies on its management of software development activities. Not only does the technological functionality of a blockchain system depend on the quality of its source code, but also the “neutrality” of a blockchain system depends on access to and control over its source code. In this paper, we delve into the dynamic evolution of collaboration within the open-source developer community of Ethereum, a leading blockchain platform today. Utilizing network analysis techniques, we demonstrate the tendency of initially decentralized open-source communities to gravitate toward local modular structures. Additionally, our analysis reveals that inputs to the blockchain’s software code are concentrated among a small group of contributors. Our findings have implications for the managers of decentralized open-source technology and policymakers who aim to preserve impartial access to technology.
In order to meet the demand for blockchain talents, this paper explores the practical teaching of blockchain technology.Taking Ethereum as the platform and combining the application of blockchain technology in the field of digital copyright protection, we guide students to conduct research projects, so that students have a deep understanding of some abstract concepts of blockchain and master the methods of blockchain development.
Blockchain has been broadly recognized as a breakthrough technology of the world. Web3, recently, is emerging as a buzzword, indicating the next generation of Internet based on Blockchain, envisioning the Internet of Money to store and transfer value. However, when people want a comprehensive view throughout advancements in the Blockchain space, there is a missing in the academic domain and scientific publications regarding distributed ledger technology (DLT) classification and taxonomy for the evolution of public Blockchain generations. In this research, the author attempts to classify DLTs in terms of data structure (ledger type), governance and accessibility. Furthermore, based on the well-known problems and the most technical challenges in Blockchain space, the author studies breaking and significant inventions of various blockchain protocols to give a taxonomy for the evolution of four public Blockchain generations, blockchain layers (0, 1, 2, 3). The first and second generations are dominated by Bitcoin and Ethereum, respectively. The latest state-of-the-art blockchain protocols are developing and shaping the third and fourth generations, where several ``Ethereum-killer" candidates are trying to solve major problems, to offer fantastic functions and capacity, by their own outstanding innovations and distinguished architectural designs. This work helps readers quickly capture historical evolution and innovations of Blockchain, envisioning the next advancements of Web3 as well as the Internet of Value (Internet 2.0).
We present a general purpose, privacy-preserving framework for verifying user attributes. The framework is designed for users (e.g., a job candidate) to allow a challenger (e.g., a prospective employer) to verify whether the usermeets a particular requirement (e.g., does the candidate hold a valid driving license?), without leaking any other information about the user. Importantly, the user is an active part of the challenge-verification process, which ensures that challenges cannot be made without the user’s full knowledge and participation. The framework is decentralized and requires a public blockchain. A smart contract is used to manage the challenge-verification process, and zero-knowledge proofs are used to verify challenges in a privacy-preserving manner. We implement a simplified version of the framework using smart contracts deployed on the Ethereum blockchain, and we simulate some simple use cases. All simulation code is available open-source (https://github.com/lifeisbeer/BlockVerify).
Blockchain explorers are important tools for quick look-ups of on-chain activities. However, as centralized data providers, their reliability remains under-studied. As a case study, we investigate Beaconcha.in, a leading explorer serving Ethereum's proof-of-stake (PoS) update. According to the explorer, we find that more than 75\% of slashable Byzantine actions were not slashed. Since Ethereum relies on the "stake-and-slash" mechanism to align incentives, this finding would at its face value cause concern over Ethereum's security. However, further investigation reveals that all the apparent unslashed incidents were erroneously recorded due to the explorer's mishandling of consensus edge cases. Besides the usual message of using caution with centralized information providers, our findings also call for attention to improving the monitoring of blockchain systems that support high-value applications.
Cryptocurrencies are digital assets that are widely used for trading and investing. One of the characteristics that traders take advantage of for profit is the high volatility of the price. Its volatile and rapidly changing prices have made cryptocurrency price predictions a challenging and highly sought-after research topic. Cryptocurrency price predictions usually only use historical prices on the dataset, while price movements are also influenced by other aspects such as sentiment contained in social media. This study proposes a new machine learning method to predict Ethereum and Solana cryptocurrency price, which integrates cryptocurrency historical price data and social media sentiment as inputs of the prediction model. FinBERT, a pre-trained sentiment analysis model is used to extract the sentiment implied in social network tweets into daily sentiment score, which are then combined with the historical market price data. The hybrid model of LSTM-GRU model is used to train the dataset and perform cryptocurrency price prediction. The experiment results show that the presented method can successfully predict the Ethereum and Solana price movement and has superior performance than all the benchmark models.
Deep learning methods have achieved significant success in various applications, including trend signal prediction in financial markets. However, most existing approaches only utilize price action data. In this paper, we propose a novel system that incorporates multiple data sources and market correlations to predict the trend signal of Ethereum cryptocurrency. We conduct experiments to investigate the relationship between price action, candlestick patterns, and Ethereum-Bitcoin correlation, aiming to achieve highly accurate trend signal predictions. We evaluate and compare two different training strategies for Convolutional Neural Networks (CNNs), one based on transfer learning and the other on training from scratch. Our proposed 1-Dimensional CNN (1DCNN) model can also identify inflection points in price trends during specific periods through the analysis of statistical indicators. We demonstrate that our model produces more reliable predictions when utilizing multiple data representations. Our experiments show that by combining different types of data, it is possible to accurately identify both inflection points and trend signals with an accuracy of 98%.
Phi Dinh Hoang, Emmanuel L. C. VI M. Plan, Nguyễn Thị Ngà
PurposeAs an emerging technological and financial asset, non-fungible tokens (NFT) have yet to be completely understood and, owing to bubble-like behavior along with pandemic effects, literature has mostly focused on pricing mechanisms and spillover effects between cryptocurrency and NFTs.This work aims to determine the significant determinants of trading activity in NFT collections quantified by the number of transactions as a means to characterize NFTs investors behaviours. Design/methodology/approachNFTs collection-week data from the Ethereum blockchain were scraped.Together with collection-specific features, we employ factor analysis and rich regression estimation to identify the factors that are significantly correlated to trading activity. FindingsOur results, consistent across the different models and methodologies, indicate that the historical transaction and pricing as well as Ethereum market activity are the important determinants of NFTs trading activity.Certain collection-specific properties also show significant relationship with NFTs trading activities, such as age, size, creators commision, collection attention, and utility. Research limitations/implicationsOur work calls for more studies to fully understand the effect of wash trades and on NFT market activity. Practical implicationsOur results give recommendations to NFT investors and can guide them in performing sensible investment decisions at the level of selecting potential liquid and valuable collections.Given NFTs trading activity closely associate with NFTs market liquidity, understanding the determinants of NFTs trading provide important implications for preventing market failure.
Astrid Novita Putri, Mochamad Hariadi, Reza Fuad Rachmadi
Smart contracts and Blockchain are necessary for transactions in supply chain management. A smart contract is an agreement in the form of a cryptographic code digitally signed by the actors. This technology holds the data transaction transparently and securely between the actors. The actors may benefit without intermediaries involved in the transaction. Supply chain management will be easily tracked by decentralized data sharing. Supply chain management involves transactions in agricultural sectors such as farmers, Village Cooperatives units, distributors, and consumers. The Cooperative is a party that buys goods from farmers and sells them to consumers. A smart contract is a digital signature that can access the system in transactions. The system records log activities that show the origin of transactions. All actors can see the transaction of agricultural products stored in the blockchain ledger in the Blockchain. This paper discusses blockchain-based smart contracts scenario for supply chain management that is simulated and implemented in the Serious Game. The Game is developed using Unity 3D Development Platform, programming language contract using Solidity, Moralis as Web3 Development Platform and dApp as backend blockchain, Network Environment Polygon Ethereum, and Database that is Redis Enterprise, MongoDB Compass, and Mumbai Testnet.
A. Gómez Ramírez, Loui Al Sardy, Francis Gomez Ramirez
Blockchain security is becoming increasingly relevant in today's cyberspace as it extends its influence in many industries. This paper focuses on protecting the lowest level layer in the blockchain, particularly the P2P network that allows the nodes to communicate and share information. The P2P network layer may be vulnerable to several families of attacks, such as Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. This layer is prone to threats inherited from traditional P2P networks, and it must be analyzed and understood by collecting data and extracting insights from the network behavior to reduce those risks. We introduce Tikuna, an open-source tool for monitoring and detecting potential attacks on the Ethereum blockchain P2P network, at an early stage. Tikuna employs an unsupervised Long Short-Term Memory (LSTM) method based on Recurrent Neural Network (RNN) to detect attacks and alert users. Empirical results indicate that the proposed approach significantly improves detection performance, with the ability to detect and classify attacks, including eclipse attacks, Covert Flash attacks, and others that target the Ethereum blockchain P2P network layer, with high accuracy. Our research findings demonstrate that Tikuna is a valuable security tool for assisting operators to efficiently monitor and safeguard the status of Ethereum validators and the wider P2P network
Abstract Efficiency is a fundamental property of any type of program, but it is even more so in the context of the programs executing on the blockchain (known as smart contracts ). This is because optimizing smart contracts has direct consequences on reducing the costs of deploying and executing the contracts, as there are fees to pay related to their bytes-size and to their resource consumption (called gas ). Optimizing memory usage is considered a challenging problem that, among other things, requires a precise inference of the memory locations being accessed. This is also the case for the Ethereum Virtual Machine (EVM) bytecode generated by the most-widely used compiler, , whose rather unconventional and low-level memory usage challenges automated reasoning. This paper presents a static analysis, developed at the level of the EVM bytecode generated by , that infers write memory accesses that are needless and thus can be safely removed. The application of our implementation on more than 19,000 real smart contracts has detected about 6,200 needless write accesses in less than 4 hours. Interestingly, many of these writes were involved in memory usage patterns generated by that can be greatly optimized by removing entire blocks of bytecodes. To the best of our knowledge, existing optimization tools cannot infer such needless write accesses, and hence cannot detect these inefficiencies that affect both the deployment and the execution costs of Ethereum smart contracts.
The organ donation and transplantation methods in use today present a variety of requirements and barriers as well as technical, clinical, ethical, and legal limitations regards registration, matching of donors and recipients, organ procurement, delivery, and transplantation. In order to provide a fair and effective procedure, a complete framework for organ donation and transplantation is necessary as well as to enhance patient satisfaction and trust. In this paper, we describe a completely decentralised, secure, trackable, auditable, private, and trustworthy private Ethereum blockchain-based system for coordinating organ donation and transplantation. We create smart contracts and outline the development, testing, and verification of six algorithms. By performing research on privacy, security, and confidentiality and contrasting our solution with the available alternatives, we assess the effectiveness of the suggested solution.
Represented by two digital currencies, Bitcoin and Ethereum, the dynamic characteristics of the currency value of digital currencies are analyzed.First, we conduct a horizontal analysis of the relationship between these two digital currencies through co-integration and Granger causality test.Secondly, the Sup ADF and GSADF test methods are used to longitudinally analyze the bubbles of the three digital currencies.The results show that there are Granger causality and price bubbles in Bitcoin and Ethereum.Through analysis, it is found that the high volatility of currency value is not only related to the relationship between digital currencies but also related to factors such as digital currency market fluctuations and regulatory policies.
This research paper presents an innovative approach to address the challenges associated with platitudinous paper certificates and electronic certificates. These challenges include issues such as preservation, management, verification inconvenience, poor reliability, and susceptibility to counterfeiting and tampering. The suggested approach involves an autonomous certificate system leveraging blockchain technology and smart contracts. Tailored for university students engaged in innovation and entrepreneurship competitions, this platform is intended to offer blockchain-based certification services. Within this system, various functions related to certificates, such as management, issuance, verification, and revocation, are executed through smart contracts. Structured data within these contracts stores information about signers, certificate templates, and certificate details. This structure simplifies the process of querying and validating certificates, all the while maintaining their security and authenticity.