We empirically examine the intraday return- and volatility-forecasting power of on-chain flow data for Bitcoin(BTC), Ethereum(ETH), and Tether(USDT). We find ETH net inflows to strongly predict ETH returns and volatility in the 2017-2023 period. Our intraday frequencies are 1-6 hours. We find that differing significantly from forecasting patterns for BTC, ETH net inflows negatively predict ETH returns and volatility. First, we find that USDT flowing out of investors wallets and into cryptocurrency exchanges, namely, USDT net inflows into the exchanges, positively predicts BTC and ETH returns at multiple intervals and negatively predicts ETH volatility at various intervals and BTC volatility at the 6-hour interval. Second, we find that ETH net inflows negatively predict ETH returns and volatility for all intraday intervals. Third, BTC net inflows generally lack predictive power for BTC returns(except at 4 hours) but are negatively associated with volatility across all intraday intervals. We illustrate our findings on return forecasting via case studies. Moreover, we develop option strategies to assess profits and losses on ETH investments based on ETH net inflows. Our findings contribute to the growing literature on on-chain activity and its asset pricing implications, offering economically relevant insights for intraday portfolio management in cryptocurrency markets.
Cryptocurrency has transformed from a niche interest to a significant investment domain, attracting diverse investors globally. However, the cryptocurrency market is marked by extreme price volatility and risk, particularly for major digital currencies like Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). This analysis dives deeply into the historical behavior of these cryptocurrencies, examining their volatility, returns, interdependencies, and potential predictability using advancedmodeling approaches.
Adla Padma, Mangayarkarasi Ramaiah, Vinayakumar Ravi
Of lately, the traditional agricultural sector has lags behind in adopting cutting-edge technologies, leading to challenges such as delayed remuneration for farmers, inadequate pre-purchase information for consumers, and increased retail prices due to intermediaries and processors. However, blockchain is a prominent solution for handling the consequences in agriculture by incorporating its inherent features like immutability, transparency, and highly security. The possible use of ethereum smart contracts and blockchain technology to address these challenges and enhance automation and trust in the agriculture sector. This chapter focuses on the pre- and post-harvesting phases of agriculture, utilizing blockchain as the underlying infrastructure. In field-level data is collected through IoT devices; second, ethereum smart contracts automate the transactions among participating entities. Finally, we analyzed the cost of each operation. The possible applications of IoT devices, blockchain, and smart contracts showcase significant potential to revolutionize various aspects of agriculture. By enabling enhanced automation and establishing trust among stakeholders, this approach facilitates timely remuneration for farmers, tracing food products, empowers consumers with comprehensive pre-purchase information, and mitigates price inflation introduced by intermediaries and processors. Finally, we discussed the challenges and future directions of the agriculture sector.
Smart contracts are self-executing digital contracts that run on a blockchain network. They enable the automation and decentralization of various operations and have become increasingly popular in recent years. However, smart contracts are susceptible to vulnerabilities, and their deployment without proper security testing can result in severe consequences, such as financial losses and reputational damage. In this article, we explore the use of deep learning techniques, particularly Convolutional Neural Networks (CNNs), for detecting and classifying vulnerabilities in smart contracts deployed on the Ethereum main net. We compare different kinds of neural architectures, i.e., a baseline LSTM, multiple 1D CNNs working on the smart contracts’ bytecode, a Vision Transformer (Swin v2 Tiny), and various 2D CNNs that work on RGB images obtained from the bytecode (i.e., ResNet-50, ResNeXt-50, Inception v3, and EfficientNetv2 Small). We provide an in-depth analysis of these techniques to classify a dataset of smart contracts we have collected. Our study shows that the use of deep neural networks can represent a promising technique to automatically assess smart contracts’ correctness and classify potential vulnerabilities. According to our experiments, the ResNet 1D CNN working directly on the smart contract bytecode offers the best results in terms of classification capabilities. Moreover, due to the unbalanced sizes of the different classes, the classification resulted in more effectiveness for the unchecked calls and reentrancy vulnerability classes while still providing good results for others.
This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders (SAE), to improve investment strategy performance. Using the Sharpe and Information Ratios, it specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns. The study focuses on Bitcoin, Litecoin, and Ethereum as the traded assets from January 1, 2016, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance.
Background: Land records have traditionally derived their credibility from a central database of local government records, with copies issued to land owners. Physical records are the only credible source of any information related to land ownership that has been in existence for a long time. However, physical records are prone to manipulation and fraud. Recently, some academic research has begun to address the potential use of blockchain technology to improve the security and reliability of land registration processes. Objective: The purpose of the present work is to propose an architecture for blockchain-based access control for distribution, ensuring information privacy. We take advantage of the benefits of blockchain technology in improving land record management while granting access to electronic data through user permissions. Methods: This approach replicates cryptographic primitives, while smart contracts are used to assist land record owners and users in interacting with each other using the Ethereum blockchain in the proposed system. The approach includes performance evaluation by the execution of a smart contract and security analysis to check the system robustness. Results: The performance evaluation and security analysis prove the proposed blockchain architecture to be secure and feasible for practical implementation in managing land records. Conclusion: The research proves how the application of blockchain technology can significantly enhance both security and reliability in land registration processes, giving credibility to tamper-resistant systems for maintaining information about land ownership.
Attribute-Based Encryption (ABE) is a promising solution for access control in cloud services. However, the heavy decryption overhead hinders its widespread adoption. A general approach to address this issue is to outsource decryption to decryption cloud service(DCS). Existing schemes have utilized various methods to enable users to verify outsourced results; however, they lack an effective mechanism to achieve exemptibility which enables the honest DCS to escape from wrong claims. And it is impractical to assume that the DCS will provide free services. In this paper, we propose a blockchain-based payable outsourced decryption ABE scheme that achieves both verifiability and exemptibility without adding redundant information to ABE ciphertext. We use zero-knowledge proof to verify outsourced results on blockchain and introduce an optional single-round challenge game under optimistic assumption to address the high cost of proof generation. Moreover, our system achieves fairness and decentralized outsourcing to protect the interests of all parties. Finally, we implement and evaluate our scheme on Ethereum to demonstrate its feasibility and efficiency, the gas usage in attribute numbers from 5 to 60 is 11$\times$ to 140$\times$ in the happy case and 4$\times$ to 55$\times$ in the challenge case lower than the scheme of Ge et al. (TDSC'23).
As a key enabler of Web3, Ethereum has long faced scalability challenges. The recent EIP-4844 upgrade aims to alleviate the scalability issue by introducing the ''blob'', a new data structure for Layer-2 rollups that enables off-chain storage with much reduced costs. Yet, this new mechanism's impact on Ethereum, and the wider Web3 ecosystem, remains largely underexplored. In this paper, we conduct the first large-scale empirical analysis of the post-EIP-4844 ecosystem, leveraging a dataset of 319.5 million transactions, out of which 1.3 million are blob-carrying. Our analysis reveals two major trends: (1) average block size has increased 2.5 times, from 150 KB to 400 KB, while the share of conventional transactions has shrunk from over $150$ KB to around 80 KB; (2) rollups are rapidly migrating from expensive calldata, falling from approximately 7,500 to nearly zero, toward cheap blobs, rising from zero to about 10,000. These shifts introduce a new economic game between block builders and rollups. Thus, we develop a game-theoretic model to characterize their equilibrium strategies: a profit-maximizing inclusion rule for builders, and a cost-minimizing blob batching strategy for rollups. Empirically, however, we find notable economic inefficiencies: for example, 29.48% of blob-containing blocks are built sub-optimally, yielding less revenue than available alternatives. These findings highlight the intricacies of the blob marketplace, and our work has established both methodological and empirical foundations to understand the evolving post-EIP4844 Ethereum ecosystem.
This paper summarises the intersection of Web3 and AI technologies, synergies between these technologies, and gaps that we suggest exist in the conception of the possible integrations of these technologies. The summary is informed by a comprehensive literature review of current academic and industry papers, analyst reports, and Ethereum research community blogposts. We focus our contribution on the perceived gaps and detail some novel approaches that would benefit the blockchain/Web3 ecosystem. We believe that the overview presented in this paper will help guide researchers interested in the intersection of Web3 and AI technologies.
Blockchains have sparked global interest in recent years, gaining importance as they increasingly influence technology and finance.This thesis investigates the robustness of blockchain protocols, specifically focusing on Ethereum Proof-of-Stake. We define robustness in terms of two critical properties: Safety, which ensures that the blockchain will not have permanent conflicting blocks, and Liveness, which guarantees the continuous addition of new reliable blocks.Our research addresses the gap between traditional distributed systems approaches, which classify agents as honest or Byzantine (i.e., malicious or faulty), and game-theoretic models that consider rational agents driven by incentives. We explore how incentives impact the robustness with both approaches.The thesis comprises three distinct analyses. First, we formalize the Ethereum PoS protocol, defining its properties and examining potential vulnerabilities through a distributed systems perspective. We identify that certain attacks can undermine the system's robustness. Second, we analyze the inactivity leak mechanism, a critical feature of Ethereum PoS, highlighting its role in maintaining system liveness during network disruptions but at the cost of safety. Finally, we employ game-theoretic models to study the strategies of rational validators within Ethereum PoS, identifying conditions under which these agents might deviate from the prescribed protocol to maximize their rewards.Our findings contribute to a deeper understanding of the importance of incentive mechanisms for blockchain robustness and provide insights into designing more resilient blockchain protocols.
Open access
Economic and Technological Systems Analysis
Economic and Technological Developments in Russia
Advanced Research in Systems and Signal Processing
The acceleration of the globalization process and the structural changes in technology that emerged in the 2000s have affected financial markets. This interaction in the financial markets has made the emergence of new financial assets necessary. According to the ARDL boundary test results, there is no significant relationship between cryptocurrency markets and stock returns in both the long and short term for the UK financial markets. For the German financial markets, it has been determined that there is a significant and positive long-term relationship between the cryptocurrency market assets Bitcoin and Tether and stock market returns. In the short term, no significant relationship has been detected. For the long term in the U.S. financial markets, it has been determined that there is a significant and positive relationship between Bitcoin, a cryptocurrency market asset, and stock market returns, while there is no significant relationship between Ethereum and Tether with stock market returns. In the short term, no significant relationship has been detected. These findings offer significant implications for policymakers, investors, and market analysts.
Geet Bawa, Harmeet Singh, Sita Rani, Aman Kataria · 5 authors
Background/Objectives: The healthcare sector is rapidly growing, aiming to promote health, provide treatment, and enhance well-being. This paper focuses on the organ donation and transplantation system, a vital aspect of healthcare. It offers a comprehensive review of challenges in global organ donation and transplantation, highlighting issues of fairness and transparency, and compares centralized architecture-based models and blockchain-based decentralized models. Methods: This work reviews 370 publications from 2016 to 2023 on organ donation management systems. Out of these, 85 publications met the inclusion criteria, including 67 journal articles, 2 doctoral theses, and 16 conference papers. About 50.6% of these publications focus on global challenges in the system. Additionally, 12.9% of the publications examine centralized architecture-based models, and 36.5% of the publications explore blockchain-based decentralized models. Results: Concerns about organ trafficking, illicit trade, system distrust, and unethical allocation are highlighted, with a lack of transparency as the primary catalyst in organ donation and transplantation. It has been observed that centralized architecture-based models use technologies such as Python, Java, SQL, and Android Technology but face data storage issues. In contrast, blockchain-based decentralized models, mainly using Ethereum and a subset on Hyperledger Fabric, benefit from decentralized data storage, ensure transparency, and address these concerns efficiently. Conclusions: It has been observed that blockchain technology-based models are the better option for organ donation management systems. Further, suggestions for future directions for researchers in the field of organ donation management systems have been presented.
Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.
Smart contracts have emerged as a fundamental component in the dynamic realm of blockchaintechnology, facilitating automated transactions and agreements within an environment devoid of trust. Nevertheless, the irreversible characteristic of blockchain ensures that any vulnerabilities in the code of a smart contract are not only permanent but also potentially exploitable; this emphasizes the critical requirement for thorough verification. This thesis presents the notion of utilizing a model checking framework as an essential instrument in the process of verification. Model checking provides an all-encompassing and automated methodology for identifying errors, vulnerabilities, and deviations from intended behavior in smart contracts. This ensures the dependability and security of such contracts in the era of digitalization. This thesis presents VeriSolid, a model verification framework designed specifically for inter- acting smart contracts. A Solidity Deployment Diagram (SDD) is incorporated into the framework to enable the direct deployment of verified contracts to a blockchain. This endeavor is dedicated to the verification and establishment of standards pertaining to templated contracts, such as ERC20 and ERC721. The experimental findings indicate that 4% of ERC20 contracts and 18% of ERC721 contracts on the Ethereum blockchain fail to satisfy the stipulations of the aforementioned standards, rendering them susceptible to attacks. The subsequent area of emphasis in this thesis pertains to the construction of a framework for model checking based on natural language. This framework has the capability of generating verified smart contracts that validate to a specification. The aforementioned framework additionally incor- porated the modeling of access control policies and the establishment of a rigid sequence for smart contract actions when required via the Contract Priority Diagram (CPD). Using this framework, four categories of Move smart contracts have been generated and verified. A gas optimization methodology for smart contracts has been proposed. An implementation of the approach utilizing off-the-shelf tools is presented. 72 solidity smart contracts were optimized. The average reduction in gas expenses per transaction is around 23, 943 gas units.
Muhammad Muzammil, Zhengyu Wu, Aruna Balasubramanian, Nick Nikiforakis
Ethereum Name Service (ENS) domains allow users to map human-readable names (such as gold.eth) to their cryptocurrency addresses, simplifying cryptocurrency transactions. Like traditional DNS domains, ENS domains must be periodically renewed. Failure to renew leads to expiration, making them available for others to register (a phenomenon known as dropcatching). This presents a security risk where attackers can register expired domains to leverage the residual trust associated with them and, in the context of ENS, receive transactions intended for their previous owners. In this paper, we conduct the first large-scale study on dropcatching in ENS domains. We curate and analyze a dataset comprising 3.1M ENS domains and 9.7M Ethereum transactions, finding that 241K of these domains were re-registered by new owners after expiration. Our findings indicate a preference for domains linked to high-income wallets in re-registrations. We identify 2,633 transactions that were misdirected to new owners, averaging the equivalent of thousands of US dollars. Lastly, we highlight the lack of countermeasures by digital wallet providers, and suggest straightforward approaches that they can use to minimize financial losses due to ENS dropcatching.
The agricultural sector is a vital component of Bangladesh's economy, but its agri-food supply chain faces signifi-cant inefficiencies primarily due to the involvement of numerous intermediaries. This complexity not only reduces the profits for farmers but also affects the overall transparency and efficiency of the supply chain. This study aims to em-ploy blockchain technology to transform the traditional agri-food supply chain in Bangladesh, focusing on increasing transparency, enhancing efficiency, and improving profitability for farmers, thus potentially bolstering the entire agri-food ecosystem in the country. The research involves setting up a blockchain-based smart contract on the Ethereum Blockchain network. This approach guarantees that all transactions within the agri-food supply chain are transparent, traceable, and accountable. Additionally, the study develops a web application to facilitate user interaction with the smart contract, enhancing accessibility and usability. Performance analysis and testing of the implemented smart con-tract demonstrate its capability to handle a significant volume of transactions without compromising on performance. The solution effectively reduces the dependency on intermediaries, thereby increasing the profit margins for the farm-ers involved. The integration of blockchain technology in the agri-food supply chain has shown promising results in enhancing transparency and efficiency. It lays a solid foundation for future improvements and suggests a scalable model that could be applied to other sectors within the country to further enhance agricultural practices and economic growth.
Hanen Ben Ameur, Fouad Jamaani, Mohammed N. Abu-Alfoul
This paper examines gold and cryptocurrencies' hedge and safe-haven capabilities against various downturns, including the COVID-19 pandemic and Geopolitical Risks (GPR), across different market conditions. The study covers a sample period from 2013 to 2021 at a daily frequency, employing the GARCH model and quantile regression with binary variables. The empirical results indicate that neither gold nor cryptocurrencies can act as strong hedges against infectious disease pandemics. However, gold, Bitcoin, and Ethereum exhibit weak safe-haven abilities during geopolitical risks. Using regression quantiles, the study finds that gold demonstrates a strong safe-haven against low and high Infectious Disease Epidemic Market Volatility (IDEMV) during extremely bearish and bullish markets. In contrast, Bitcoin and Ethereum act as strong safe havens only against low IDEMV during extreme bearish markets. Gold also shows a strong hedge propriety against extreme geopolitical events, while cryptocurrencies provide a weak hedge. Overall, gold exhibits strong safe-haven properties against low and high Geopolitical tensions, while cryptocurrencies' hedging and safe-haven abilities vary across markets. These findings convey insights for investors and guidance to supervisors on the evolution of gold, Bitcoin, and Ethereum as safe-haven and hedge instruments during both bearish and bullish markets.
Francesco D’Amato, Roberto Saltini, Thuy-An Tran, Luca Zanolini
Gasper, the consensus protocol currently employed by Ethereum, typically requires 64 to 95 slots -- the units of time during which a new chain extending the previous one by one block is proposed and voted -- to finalize. This means that under ideal conditions -- where the network is synchronous, and all chain proposers, along with more than two-thirds of the validators, behave as dictated by the protocol -- proposers construct blocks on a non-finalized chain that extends at least 64 blocks. This exposes a significant portion of the blockchain to potential reorganizations during changes in network conditions, such as periods of asynchrony. Specifically, this finalization delay heightens the network's exposure to Maximum Extractable Value (MEV) exploits, which could undermine the network's integrity. Furthermore, the extended finalization period forces users to balance the trade-off between economic security and transaction speed. To address these issues and speed up finality, we introduce a partially synchronous finality gadget, which we combine with two dynamically available consensus protocols -- synchronous protocols that ensure safety and liveness even with fluctuating validator participation levels. This integration results in secure ebb-and-flow protocols [SP 2021], achieving finality within three slots after a proposal and realizing 3-slot finality.
Ethereum, developed by Vitalik Buterin in 2013, has significantly advanced blockchain technology through smart contracts and ERC-20 token standards. This study examines the impact of Ethereum on ERC-20 tokens using Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) models. For this purpose, LSTM and CNN models were trained using Ethereum data and then employed to predict ERC-20 token prices. According to the study's results, the LSTM model achieved high accuracy rates for LINK, MATIC, and UNI tokens but performed poorly in predicting RNDR token prices. The CNN model provided the highest accuracy for LINK tokens and yielded successful results in predicting RNDR token prices. However, the CNN model showed lower performance for MATIC and UNI tokens than the LSTM model. These findings indicate that both LSTM and CNNmodels significantly impact the prediction of Ethereum's ERC-20 token price dynamics. The variability in model performances across tokens highlights the influence of market dynamics and liquidity levels. In light of these differences, the study emphasizes the importance of selecting the model based on the token's characteristics and market conditions.
This paper presents a comprehensive analysis of storage proofs in the Ethereum ecosystem, examining their role in addressing historical and cross-chain state access challenges. We systematically review existing approaches to historical state verification, comparing Merkle Mountain Range (MMR) and Merkle-Patricia trie (MPT) architectures. An analysis involves their respective performance characteristics within zero-knowledge contexts, where performance challenges related to Keccak-256 are explored. The paper also examines the cross-chain verification, particularly focusing on the interactions between Ethereum and Layer 2 networks. Through careful analysis of storage proof patterns across different network configurations, we identify and formalize three architectures for cross-chain verification. By organizing this complex technical landscape, this analysis provides a structured framework for understanding storage proof implementations in the Ethereum ecosystem, offering insights into their practical applications and limitations.
The application of Ethereum Blockchain within IoT-based weather monitoring systems presents substantial potential for enhancing data security, integrity, transparency, and trust. This study is focused on the design, implementation, and evaluation of Ethereum Blockchain as a robust data security mechanism in an IoT weather monitoring system. The system is configured to monitor environmental parameters, specifically temperature and humidity, using DHT22 sensors, with data securely stored and processed through smart contracts on a locally deployed Ethereum network. The research utilizes the Proof of Authority consensus mechanism, assessing data transmission and storage latency across varying mining intervals. The findings reveal minimal transmission delays, whereas storage delays on the blockchain exhibit variability, influenced by the duration of the mining period. Specifically, longer mining intervals contribute to increased delays in data storage. These results underscore the necessity of optimizing the mining interval to ensure complete and synchronized data storage, thereby enhancing the accuracy and reliability of the weather monitoring system. This study demonstrates the efficacy of Ethereum Blockchain in addressing critical challenges related to data security and integrity within IoT applications, highlighting its potential as a promising solution for secure data management.
This study investigated the impact of the COVID-19 pandemic on two cryptocurrencies, Bitcoin and Ethereum, on the one hand, and gold, on the other hand. We analysed a large dataset from 13 countries worldwide. This study aimed to identify a reliable safe haven for investors during a health crisis. Our results indicated a positive association between Bitcoin and Ethereum prices and the COVID-19 variables. However, the relationship between gold prices and COVID-19 health indicators differed among countries, with inconsistent results.
Cyber-Physical Systems (CPS) have become a research hotspot due to their vulnerability to stealthy network attacks like ZDA and PDA, which can lead to unsafe states and system damage. Recent defense mechanisms for ZDA and PDA often rely on model-based observation techniques prone to false alarms. In this paper, we present an innovative approach to securing CPS against Advanced Persistent Threat (APT) injection attacks by integrating machine learning with blockchain technology. Our system leverages a robust ML model trained to detect APT injection attacks with high accuracy, achieving a detection rate of 99.89%. To address the limitations of current defense mechanisms and enhance the security and integrity of the detection process, we utilize blockchain technology to store and verify the predictions made by the ML model. We implemented a smart contract on the Ethereum blockchain using Solidity, which logs the input features and corresponding predictions. This immutable ledger ensures the integrity and traceability of the detection process, mitigating risks of data tampering and reducing false alarms, thereby enhancing trust in the system's outputs. The implementation includes a user-friendly interface for inputting features, a backend for data processing and model prediction, and a blockchain interaction module to store and verify predictions. The integration of blockchain with Machine learning enhances both the precision and resilience of APT detection while providing an additional layer of security by ensuring the transparency and immutability of the recorded data. This dual approach represents a substantial advancement in protecting CPS from sophisticated cyber threats.