ABSTRACT Ensuring the security and privacy of sensitive health data in Internet of Things (IoT)‐based healthcare systems (HCS) is a critical challenge. This paper proposes a robust security framework by integrating blockchain mechanisms and deep learning (DL) approaches to enhance security and data privacy. The proposed framework leverages the Ethereum blockchain with zero knowledge proof (ZKP) to ensure data integrity and confidentiality, while the interplanetary file system (IPFS) provides secure and efficient data storage. Additionally, a novel At‐GAN‐BiLSTM model is introduced for intrusion detection by combining the attention mechanism, generative adversarial networks (GAN) and bidirectional long short‐term memory (Bi‐LSTM) to improve detection accuracy and also help to enhance model robustness. The proposed model is evaluated by two different benchmark datasets, namely CICIDS‐2018 (D1) and ToN‐IoT (D2), achieving accuracies of 99.9% and 99.1%, respectively. Comparative investigation shows that the proposed approach reduces false alarm rates (FAR) and performs better than current models in identifying impersonation, insider, and man‐in‐the‐middle (MITM) attacks. By integrating blockchain and DL, the proposed framework significantly enhances intrusion detection, data security, and overall system resilience, addressing key vulnerabilities in IoT‐based healthcare security.
Francisco Gomes Figueira, Martin Derka, Ching Lun Chiu, Jan Gorzny
A rollup network is a type of popular "Layer 2" scaling solution for general purpose "Layer 1" blockchains like Ethereum. Rollups networks separate execution of transactions from other aspects like consensus, processing transactions off of the Layer 1, and posting the data onto the underlying layer for security. While rollups offer significant scalability advantages, they often rely on centralized operators for transaction ordering and inclusion, which also introduces potential risks. If the operator fails to build rollup blocks or propose new state roots to the underlying Layer 1, users may lose access to digital assets on the rollup. An escape hatch allows users to bypass the failing operator and withdraw assets directly on the Layer 1. We propose using a time-based trigger, Merkle proofs, and new resolver contracts to implement a practical escape hatch for these networks. The use of novel resolver contracts allow user owned assets to be located in the Layer 2 state root, including those owned by smart contracts, in order to allow users to escape them. This design ensures safe and verifiable escape of assets, including ETH, ERC-20 and ERC-721 tokens, and more, from the Layer 2.
Libraries are actively exploring innovative methods to leverage advanced technological breakthroughs like Blockchain, driven by the rapid evolution of information technology. The inherent benefits of blockchain, including its decentralized, transparent, and safe data management capabilities, offer compelling solutions for various library operations. While Bitcoin remains a prominent application, libraries can harness Blockchain's underlying potential to significantly enhance efficiency and security across numerous facets of their services. This study delves into the multifaceted potential effects of blockchain technology on libraries. It meticulously examines possible applications, long-term benefits, and the critical significance of seamlessly integrating blockchain technology into existing library services. For instance, blockchain could revolutionize interlibrary loan systems by creating an immutable, tamper-proof record of every transaction, thereby drastically reducing administrative burdens, minimizing disputes over borrowed materials, and expediting the overall lending process. Furthermore, it offers a robust framework for enhanced intellectual property management for digital resources, ensuring that creators' rights are meticulously protected and providing transparent, auditable tracking of digital content usage. This level of verifiable provenance is particularly crucial for academic and research libraries managing vast collections of scholarly works. Ultimately, the objective is to move towards Blockchain-based library management systems that require less manual labor, thereby improving overall understanding, fostering creativity in service design, and streamlining operational efficiency. Given that blockchain adoption in libraries is still in its nascent stages, this study aims to provide insightful information that can serve as a foundational guide for future research and practical implementation. The study concludes that, by adopting a contextual approach, Blockchain technology holds immense promise for greatly enhancing the efficacy and efficiency of resource transparency, ensuring patron privacy through secure data management, and bolstering information security across all library functions. References Abdennadher, S., Grissa, D., & Hamdi, M. (2022). Blockchain in accounting and auditing: A systematic literature review. Journal of Accounting & Organizational Change, 18(2), 234-256. Abid, H. (2021). Uses of blockchain technologies in library services. Library Hi Tech News, 38(8), 9-11. Agbo, C. C., Mahmoud, Q. H., & Eklund, J. M. (2019). Blockchain technology in healthcare: A systematic review. Healthcare, 7(2), 56. Akram, S. V., Malik, P. 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Blockchain technology in libraries: A systematic review. Journal of Academic Librarianship, 46(5), 102-115. Han, J., Kim, S., & Lee, H. (2023). Blockchain in accounting: A review of applications and future directions. Journal of Accounting Literature, 45(1), 78-92. Hargaden, V., Papakostas, N., & Newell, A. (2019). Blockchain in construction: A review of applications and challenges. Automation in Construction, 102, 1-12. Hasan, R., & Landry, B. (2018). Blockchain for library collaboration: A decentralized approach. Library Trends, 67(2), 245-260. Hasselgren, A., Kralevska, K., & Gligoroski, D. (2019). Blockchain in healthcare: A systematic mapping study. IEEE Access, 7, 12345-12367. Hoy, M. (2017). An introduction to blockchain for librarians. Library Technology Reports, 53(8), 1-35. Huang, Y., Zhou, X., & Wang, X. (2018). Blockchain-based library management system: A conceptual framework. Journal of Library and Information Science, 42(3), 123-135. Irving, G., & Holden, J. (2016). 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Mrs. P Maraeswari, Navya Sri Vangala, Anu Chandana Chiluuri, Mohammad Sameer · 6 authors
Abstract: The default voting procedure has many inefficiencies such as issues with effectiveness, security, and transparency. These problems erode the confidence and credibility in the electoral frameworks which fosters conflict and skepticism towards the legitimacy of governance. A solution for voting problems is Secure Sphere, a decentralized ballot system that employs the Ethereum blockchain. Through block technology, Secure Sphere guarantees that its voting process is utterly transparent, secure, and un hackable. Votes are protected against unauthorized additions by casting them on the Ethereum blockchain. This approach mitigates most problems associated with traditional voting systems such as vote tampering and recounting, misrepresentation, and cyber threats. Moreover, the voting process is further secured by the application of cryptographic techniques. The principal feature of Secure Sphere is smart contracts which are vital in automating the voting process. Each vote is verifiable and counted, therefore, once cast, a vote becomes irrevocable. Because of these contracts the system is enhanced to enable real time vote verification, thus rendering the votes straightforwardly auditable. Therefore, both voters and election officials are able to independently confirm the outcomes.
Blockchain platform performance is critically important for financial transaction applications. This article presents a case study comparing Ethereum, a public blockchain, with Hyperledger Fabric, a permissioned blockchain, for modeling financial transactions. Key performance metrics evaluated include throughput, latency, transaction cost, and finality. Our findings show that the Ethereum network achieved approximately 15 transactions per second (TPS) with a latency of ~12 seconds and incurred transaction fees of a few U.S. dollars. In contrast, Hyperledger Fabric sustained ~2000 TPS with sub-second latency and negligible cost. Fabric’s deterministic consensus also provides near-instant finality (~1–2 s), contrasting with Ethereum’s probabilistic finality, which requires ~1 minute. The detailed empirical results in Figures and summary Tables comparing core metrics reveal that Hyperledger Fabric offers superior throughput and efficiency for enterprise financial scenarios, while Ethereum’s performance is constrained by its decentralized consensus overhead. All measurements are based on an internal case study deployment without simulation. These insights inform platform selection for financial applications requiring high transaction volume and low latency.
Sang-Hyeon Park, Jeonghyuk Lee, Seunghwa Lee, Jung Hyun Chun · 8 authors
Merging Internet (web2) identities with blockchain (web3) identities is increasingly important for enhancing user experience and ensuring regulatory compliance. However, conventional solutions that map web2 identities to web3 accounts often lead to privacy concerns and fragmented identifiers across networks. To address these challenges, we propose a new identity scheme named Address Abstraction (AA), which redefines blockchain address and signing systems while preserving key properties: uniqueness, immutability, and privacy-preservation. This approach eliminates the limitations of chain-specific identity systems, enabling users to interact with multiple blockchains using their web2 certificates and unified identifiers. This chain-agnostic identifier also promotes cross-chain compatibility. We further present Zero-Knowledge Address Abstraction (zkAA), an implementation of AA that uses zero-knowledge proofs to uphold AA's core properties. Additionally, a proof aggregation technique combines multiple proofs into one, achieving approximately 5.5 times gas cost savings during verification in real-world scenarios. As of August 2024, zkAA with proof aggregation incurs an additional cost of only $0.66 per transaction on Ethereum.
Enforcing a delay between deposits and withdrawals within decentralized finance protocols may make them more secure but less composable. A delay makes flash loan attacks more expensive, but restricts interactions between protocols. In this work, we analyse public blockchain data to determine if this concern is warranted in practice. We measure the duration between corresponding direct deposit and withdrawal function calls across several decentralized finance protocols on Ethereum. We show that direct callers of DeFi protocols typically leave assets locked in these protocols for many blocks, meaning that artificial withdrawal delays are not likely to have a negative impact on user experience.
Blockchain technology is gaining traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in health data management. However, the large volume of biomedical datasets has slowed its adoption due to poor scalability. This challenge is especially relevant for applications that rely on blockchain's strong immutability by storing data directly on-chain. In this work, we demonstrate the potential of blockchain to create a secure and trustless environment for managing large on-chain records. Specifically, we detail an efficient, index-based approach for storing data on the Ethereum blockchain. We show that insertion and retrieval speeds remain nearly constant relative to database size, scaling linearly with the amount of data processed. Additionally, we achieve substantial efficiency gains through low-level assembly optimizations on the Ethereum Virtual Machine, highlighting the limitations of the Solidity compiler. Finally, we illustrate this approach through a practical case study, by designing and implementing a smart contract for storing and querying training certificates on the Ethereum blockchain. Our solution achieves 2x faster data insertion, 500x faster retrieval, 60% lower gas costs, and 50% lower storage usage compared to baseline methods. It won first place for track 1 of the 2022 iDASH secure genome analysis competition. We also demonstrate that this solution readily adapts to other data types, enabling efficient on-chain storage and retrieval of text, RNA-seq, or biomedical image data.
Smart Contracts are the central piece of Ethereum and other compatible blockchains.Their role is to build trusted functionality that unknown parties can interact with.However, their value proposition can be undermined by different security exploits.In many cases, vulnerabilities are overlooked not due to neglect but due to a systematic approach in the review process.This paper aims to appeal to existing frameworks for understanding the business context and provide standardized thinking on auditing smart contracts.The power of a framework lies in the fact that it ensures that auditors do not overlook critical aspects of their vulnerability.
Aktham Maghyereh, Mohammad Al‐Shboul, Basel Awartani
Research background: This paper explores the hedging and safe-haven properties of gold-backed cryptocurrencies within the context of conventional cryptocurrencies such as Bitcoin, Ethereum, Tether, and Binance. With the rise of blockchain technology, cryptocurrencies have gained recognition as alternative investment assets, drawing comparisons to traditional safe-haven assets like gold. However, the risk management potential of crypto gold, especially during periods of extreme market volatility, remains under-examined. Purpose of the article: The purpose of this article is to assess the effectiveness of gold-backed cryptocurrencies as hedging instruments and safe havens for investors in conventional cryptocurrencies. By analyzing their tail dependence during extreme market fluctuations, the study aims to determine their risk management utility. Methods: To achieve this, we employ a Student’s t copula structure integrated with an ARMA-GJR-GARCH model to measure the time-varying tail dependence between gold-backed and conventional cryptocurrencies. This approach allows for a comprehensive analysis of both normal and extreme market conditions. We use the Digix Gold Token (DGX) as a representative of gold-backed cryptocurrencies. The study examines four major conventional cryptocurrencies — Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Binance (BNB) — by analyzing daily closing prices from May 14, 2018, to January 31, 2023, which comprise 1702 observations. The dataset, sourced from coincodex.com, includes periods of significant market stress, such as the COVID-19 pandemic and the Russian-Ukrainian conflict. Findings & value added: The findings reveal a weak association between gold-backed cryptocurrencies and conventional cryptocurrencies, resulting in medium-to-low hedging effectiveness during the sample period. Nevertheless, during crisis periods, a negative association is observed, indicating that gold-backed cryptocurrencies act as effective safe havens in times of market distress. The study contributes to the literature by providing empirical evidence on the risk management benefits of crypto gold, particularly during financial crises, and highlights its potential inclusion in portfolios with cryptocurrency investments to enhance resilience.
This study evaluates the effectiveness of the CNN-LSTM hybrid model in predicting the Ethereum exchange rate against the United States Dollar (USD) by comparing the performance of the model without optimization and the model with hyperparameter optimization using Bayesian Optimization. The dataset used is sourced from Yahoo Finance covering the period 2017-2023. The results show that the CNN-LSTM model with hyperparameter optimization consistently outperforms the model without optimization, with improved prediction accuracy shown through the RMSE, MAE, MAPE, and R² values. Hyperparameter optimization resulted in an optimal configuration with 166 filters, kernel size 5, 168 LSTM units, 91 dense units, learning rate 0.00114, and batch size 32. This research confirms the effectiveness of the CNN-LSTM hybrid approach in predicting crypto exchange rates, and demonstrates the importance of hyperparameter optimization in improving prediction accuracy.
In Ethereum, private transactions, a specialized transaction type employed to evade public Peer-to-Peer (P2P) network broadcasting, remain largely unexplored, particularly in the context of the transition from Proof-of-Work (PoW) to Proof-of-Stake (PoS) consensus mechanisms. To address this gap, we investigate the transaction characteristics, (un)intended usages, and monetary impacts by analyzing large-scale datasets comprising 14,810,392 private transactions within a 15.5-month PoW dataset and 30,062,232 private transactions within a 15.5-month PoS dataset. While originally designed for security purposes, we find that private transactions predominantly serve three distinct functions in both PoW and PoS Ethereum: extracting Maximum Extractable Value (MEV), facilitating monetary transfers to distribute mining rewards, and interacting with popular Decentralized Finance (DeFi) applications. Furthermore, we find that private transactions are utilized in DeFi attacks to circumvent surveillance by white hat monitors, with an increased prevalence observed in PoS Ethereum compared to PoW Ethereum. Additionally, in PoS Ethereum, there is a subtle uptick in the role of private transactions for MEV extraction. This shift could be attributed to the decrease in transaction costs. However, this reduction in transaction cost and the cancellation of block rewards result in a significant decrease in mining profits for block creators.
Cryptocurrencies have transformed financial markets with their innovative blockchain technology and volatile price movements, presenting both challenges and opportunities for predictive analytics. Ethereum, being one of the leading cryptocurrencies, has experienced significant market fluctuations, making its price prediction an attractive yet complex problem. This paper presents a comprehensive study on the effectiveness of Large Language Models (LLMs) in predicting Ethereum prices for short-term and few-shot forecasting scenarios. The main challenge in training models for time series analysis is the lack of data. We address this by leveraging a novel approach that adapts existing pre-trained LLMs on natural language or images from billions of tokens to the unique characteristics of Ethereum price time series data. Through thorough experimentation and comparison with traditional and contemporary models, our results demonstrate that selectively freezing certain layers of pre-trained LLMs achieves state-of-the-art performance in this domain. This approach consistently surpasses benchmarks across multiple metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), demonstrating its effectiveness and robustness. Our research not only contributes to the existing body of knowledge on LLMs but also provides practical insights in the cryptocurrency prediction domain. The adaptability of pre-trained LLMs to handle the nature of Ethereum prices suggests a promising direction for future research, potentially including the integration of sentiment analysis to further refine forecasting accuracy.
Blokzincir teknolojisi ve kripto paralar, günümüz dünyası finansal ekosisteminde giderek daha fazla önem kazanan unsurlardır. Blokzincir, dağıtık defter teknolojisi olarak tanımlanmakta olup, güvenli, şeffaf ve değiştirilemez bir veri yapısı sunarak merkezi otorite ihtiyacını ortadan kaldırmaktadır. Bu teknoloji sayesinde Bitcoin, Ethereum, Ripple ve Litecoin gibi dijital varlıklar, ekonomik faaliyetlerde alternatif bir araç olarak değerlendirilmeye başlanmıştır. Söz konusu teknoloji, toplumlarda hızla bir farkındalık oluşturmuş ve yatırım aracı olarak kabul edilmesi çabuklaşmıştır. Ancak bu yeni nesil finansal araçların İslam fıkhındaki konumu ve meşruiyeti üzerine ciddi tartışmalar söz konusu olmaktadır. Hızlı finansal etkileşimler, Müslüman yatırımcıların da dikkatini çekmiş; dolayısıyla kripto paraların meşruiyeti hususunda çeşitli tartışmalar gündeme gelmiştir. İslami finans prensipleri, riba (faiz), gharar (belirsizlik) ve maysir (şans oyunu) gibi unsurları yasaklamakta, bu nedenle kripto paraların doğası ve işleyişi üzerine ayrıntılı bir inceleme yapılması gerekmektedir. Bu çalışmanın ana amacı, bu belirsizlikleri ele alarak kripto para teknolojisini derinleme şeklinde olmasada genel hatlarıyla açıklamak ve İslami finans perspektifinden meşru bir araç olup olmadığını irdelemektir. Araştırma, söz konusu teknolojinin bazı İslami otoriteler ve bazı İslam alimlerinin görüşlerini inceleyip analiz etmeyi amaçlamaktadır. Blokzincir teknolojisi ve kripto paraların İslam fıkhındaki yeri, geniş bir literatüre ve tartışmaya tabi olan karmaşık bir konu olarak karşımıza çıkmaktadır. Yapılan inceleme ve araştırmalar sonucunda, bu kuruluşların ve uzmanların büyük ölçüde kripto para teknolojisine yönelik ciddi itirazları olduğu ve bu itirazlar giderilmediği sürece, İslami olarak meşru bir finansal enstrüman olarak görülemeyeceği sonucuna ulaşılmıştır. Çoğunluk görüşünü yansıtan bu yaklaşımın yanında, kripto paraların meşru olması gerektiğini savunan önemli sayıda da alim bulunmaktadır. Bu alandaki görüşlerin çeşitliliği dolayısıyla kripto para konusunun, Müslüman toplumlar arasında helal olup olmadığıyla ilgili tartışmaları bir süre daha sürdüreceği ve yeni söylemlere zemin hazırlayacağı öngörülmektedir. Ayrıca ilgili konudaki fetva, görüş vb. Müslüman yatırımcıların karar alma süreçlerinde önemli bir etken olmakla birlikte gelecekte bu konudaki çalışmaların artacağı öngörülmektedir.
Os chamados smart contracts ou “contratos inteligentes” são uma inovação tecnológica difundida a partir do lançamento da plataforma Bitcoin em 2009 e, principalmente, da plataforma Ethereum em 2014. Em princípio, eles têm como propósito automatizar a execução das obrigações das partes em um negócio jurídico, de modo a diminuir o risco de seu inadimplemento e evitar a dependência de um terceiro de confiança (seja ele um árbitro, um juiz ou mero registrador dos dados referentes à transação celebrada). O potencial uso de smart contracts em vários setores econômicos desperta questionamentos sobre a adequação das normas do direito contratual tradicional para sua regulação. No presente trabalho, busca-se analisar dois pontos específicos sobre o tema, a saber: o momento de formação do negócio jurídico segundo a disciplina trazida pelo Código Civil brasileiro e as consequências de um contrato inteligente com objeto ilegal. O trabalho revisa amostra da literatura dedicada ao tema, tanto nacional quanto estrangeira, esta última sobretudo quando provinda de autores dos Estados Unidos da América e de Estados membros da União Europeia. O texto também questiona a adequação dos novos negócios ao marco normativo brasileiro sobre negócios.
This dataset contains a single-day sample of network traffic metadata, node metrics, honeypot interactions, and node logs collected from Ethereum nodes during the period of 2024-08-19 to 2024-08-20. The dataset provides insights into Ethereum node performance, peer-to-peer traffic, and honeypot interaction patterns. A corresponding metadata.json file is included to describe the fields within this dataset. Network Traffic Sample: Captures all traffic, including honeypot interactions, with the ability to filter by port. For peer-to-peer traffic, the dataset can be filtered by the advertised P2P ports (64000 and 64333). Honeypot Logs: Includes details of honeypot interaction events, specifying the honeypot, port, and geolocation of the source. Node Metrics: Contains telemetry samples from the Nethermind and Lighthouse Ethereum clients, offering insights into node performance. Node Logs: Comprises terminal-based text log samples generated by Nethermind and Lighthouse Ethereum clients. This dataset is suitable for researchers and practitioners studying Ethereum node behavior, P2P networks, and the security implications of using Ethereum nodes as honeypots.
Blockchain technology, a decentralized and immutable ledger, has transformed identity and access management (IAM) by enhancing security, privacy, and trust in digital ecosystems. Ensuring safe authentication and data integrity is made possible by its integration with sophisticated cryptographic techniques like zero-knowledge proofs (ZKPs) and public- key infrastructure (PKI). Other methods include verifiable credentials (VCs) and decentralized identifiers (DIDs). This paper provides a comprehensive analysis of blockchain-based IAM systems, comparing leading blockchain platforms, including Ethereum, Hyperledger Indy, IOTA, and IoTeX, in identity management. The role of blockchain in mitigating identity-related threats, such as identity theft and unauthorized access, is explored through decentralization, immutability, and smart contract automation. Additionally, key security enhancements, including cryptographic mechanisms that strengthen decentralized identity solutions and privacy-preserving authentication, are examined. The potential of blockchain to establish a self-sovereign identity framework that fosters trust, scalability, and security in digital identity ecosystems is highlighted, paving the way for the next generation of identity management solutions.
Junhao Wu, Yixin Yang, Chengxiang Jin, Silu Mu · 8 authors
With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the security of account assets. Existing Ethereum fraud detection methods typically model account transactions as graphs, but this approach primarily focuses on binary transactional relationships between accounts, failing to adequately capture the complex multi-party interaction patterns inherent in Ethereum. To address this, we propose a hypergraph modeling method for the Ponzi scheme detection method in Ethereum, called HyperDet. Specifically, we treat transaction hashes as hyperedges that connect all the relevant accounts involved in a transaction. Additionally, we design a two-step hypergraph sampling strategy to significantly reduce computational complexity. Furthermore, we introduce a dual-channel detection module, including the hypergraph detection channel and the hyper-homo graph detection channel, to be compatible with existing detection methods. Experimental results show that, compared to traditional homogeneous graph-based methods, the hyper-homo graph detection channel achieves significant performance improvements, demonstrating the superiority of hypergraph in Ponzi scheme detection. This research offers innovations for modeling complex relationships in blockchain data.
In today's blockchain landscape, smart contracts are assuming a pivotal role, albeit accompanied by a heightened risk of exploitation by attackers. As smart contracts grow in complexity, vulnerabilities lurking within deeper layers of code become more prevalent. Existing analysis tools primarily focus on data flow and a priori knowledge based on symbolic execution as a test case generation strategy, often falling short in uncovering vulnerabilities nested within intricate conditional statements. To address this challenge, we present ACOFuzz, an advanced fuzzer for Ethereum smart contracts. ACOFuzz employs the ant colony optimization (ACO) algorithm to traverse the control flow graph (CFG) of smart contracts, systematically exploring execution paths and generating test cases. Subsequently, it strategically directs the search towards paths that are more susceptible to vulnerabilities within the CFG, leveraging block coverage data obtained from executing the test cases. In a comprehensive evaluation, we demonstrate that ACOFuzz excels in covering a wider array of paths within a contract while exhibiting enhanced accuracy in pinpointing specific vulnerabilities compared to contemporary fuzzers.
With the growth of the Internet of Things (IoT), millions of users, devices, and applications compose a complex and heterogeneous network, which increases the complexity of digital identity management. Traditional centralized digital identity management systems (DIMS) confront single points of failure and privacy leakages. The emergence of blockchain technology presents an opportunity for DIMS to handle the single point of failure problem associated with centralized architectures. However, the transparency inherent in blockchain technology still exposes DIMS to privacy leakages. In this paper, we propose the privacy-protected IoT DIMS (PPID), a novel blockchain-based distributed identity system to protect the privacy of on-chain identity data. The PPID achieves the unlinkability of identity-credential-verification. Specifically, the PPID adopts the Zero Knowledge Proof (ZKP) algorithm and Shamir secret sharing (SSS) to safeguard privacy security, resist replay attacks, and ensure data integrity. Finally, we evaluate the performance of ZKP computation in PPID, as well as the transaction fees of smart contract on the Ethereum blockchain.
Open access
Blockchain Technology Applications and Security
Network Security and Intrusion Detection
Advanced Steganography and Watermarking Techniques
У статті проаналізовано можливості платформи Ethereum для розроблення та впровадження смарт-контрактів в інформаційній системі обліку продажу зброї. Розглянуто архітектуру платформи, механізми забезпечення прозорості, безпеки та достовірності даних у процесах купівлі-продажу. Особлива увага приділена аналізу ключових компонентів платформи, таких як Ethereum Virtual Machine (EVM) та механізм консенсусу Proof of Stake, які забезпечують децентралізовану та захищену основу для роботи смарт-контрактів. Досліджено потенціал смарт-контрактів для автоматизації ключових етапів обліку, включаючи перевірку дозволів на придбання зброї, реєстрацію угод, контроль дотримання законодавчих норм і відстеження змін у даних у реальному масштабі часу. Визначено переваги використання блокчейн-технологій, серед яких — незмінність записів, доступність для аудиту в режимі реального часу, підвищена прозорість операцій і мінімізація людського фактору. Окремо підкреслено, що інтеграція блокчейну дозволяє створити надійні механізми запобігання несанкціонованому доступу, фальсифікації даних і шахрайству. Розглянуто приклади реалізації смарт-контрактів у системі обліку продажу зброї та описано, як автоматизація цих процесів може сприяти зменшенню адміністративного навантаження. Водночас у статті висвітлено ключові виклики впровадження системи, включаючи питання масштабованості мережі, забезпечення конфіденційності персональних даних користувачів, адаптацію існуючих законодавчих норм до блокчейн-рішень і підвищення рівня довіри між учасниками системи. Запропоновано можливі напрями подальших досліджень, зокрема щодо інтеграції смарт-контрактів з іншими цифровими технологіями для забезпечення комплексного підходу до обліку та моніторингу. Результати роботи демонструють високий потенціал використання платформи Ethereum для підвищення ефективності та прозорості процесів у сфері контролю обігу зброї. Інтеграція смарт-контрактів із сучасними інформаційними системами обліку відкриває нові можливості для створення надійних і безпечних рішень, які відповідають викликам цифрової епохи.
Smart contracts are small programs that run autonomously on the blockchain, using it as their persistent memory. The predominant platform for smart contracts is the Ethereum VM (EVM). In EVM smart contracts, a problem with significant applications is to identify data structures (in blockchain state, a.k.a. "storage"), given only the deployed smart contract code. The problem has been highly challenging and has often been considered nearly impossible to address satisfactorily. (For reference, the latest state-of-the-art research tool fails to recover nearly all complex data structures and scales to under 50% of contracts.) Much of the complication is that the main on-chain data structures (mappings and arrays) have their locations derived dynamically through code execution. We propose sophisticated static analysis techniques to solve the identification of on-chain data structures with extremely high fidelity and completeness. Our analysis scales nearly universally and recovers deep data structures. Our techniques are able to identify the exact types of data structures with 98.6% precision and at least 92.6% recall, compared to a state-of-the-art tool managing 80.8% and 68.2% respectively. Strikingly, the analysis is often more complete than the storage description that the compiler itself produces, with full access to the source code.
With the rise of blockchain technology and the Ethereum platform, non-fungible tokens (NFTs) have emerged as a new class of digital assets. The NFT transfer network exhibits core-periphery structures derived from different partitioning methods, leading to local discrepancies and global diversity. We propose a core-periphery structure characterization method based on Bayesian and stochastic block models (SBMs). This method incorporates prior knowledge to improve the fit of core-periphery structures obtained from various partitioning methods. Additionally, we introduce a locally weighted core-periphery structure aggregation (LWCSA) scheme, which determines local aggregation weights using the minimum description length (MDL) principle. This approach results in a more accurate and representative core-periphery structure. The experimental results indicate that core nodes in the NFT transfer network constitute approximately 2.3-5% of all nodes. Compared to baseline methods, our approach improves the normalized mutual information (NMI) index by 6-10%, demonstrating enhanced structural representation. This study provides a theoretical foundation for further analysis of the NFT market.
Md. Shahidul Islam, Monjira Bashir, Siddikur Rahman, Md Abdullah Al Montaser · 7 authors
The cryptocurrency market, with its record volatility and breakneck speed, is a revolutionary phenomenon that is reshaping the entire world's landscape. Unlike regular markets, cryptocurrencies undergo unprecedented volatility caused by a complex interaction of factors ranging from speculative trading to updates in regulations, technological innovations, and macroeconomic trends. The central objective of this research was to develop and evaluate machine learning-driven models of cryptocurrency price trend forecasting. The focus of this research project revolved around prominent cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), and other prominent altcoins, within the United States. The dataset employed in this analysis comprises vast historical price data, trading volumes, and key market indicators of major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), and other major altcoins. Historical price data is presented in terms of daily, hourly, and minute-level opening, closing, high, and low prices, providing detailed insights into temporal price behavior. Trading volumes, which reflect the intensity of trading action, are also provided to represent liquidity and investor participation behavior. The dataset also includes various market indicators, i.e., moving averages, relative strength index (RSI), Bollinger Bands, and other technical indicators, which play a pivotal role in establishing market patterns and momentum. Three models are chosen in this study: Logistic Regression, Random Forest Classifier, and XG Boost Classifier. For classification models, accuracy, precision, recall, and F1-score metrics are employed to evaluate the performance of the models in terms of predicting the directions of the markets (e.g., upward or downward directions). With the highest accuracy, Logistic Regression was the best-performing of the models tested, showing its relative superiority. The integration of AI forecasts into cryptocurrency trading has the potential to revolutionize the United States financial markets by providing traders and institutional investors with advanced tools to make decisions. The use of AI tools in cryptocurrency trading also has significant implications for United States regulation compliance. The integration of machine learning tools within cryptocurrency trading platforms is a significant step towards unleashing the true potential of AI in the financial markets. The field of AI-based cryptocurrency forecasting offers numerous areas of future research with the potential to break through present limitations and unlock new paths of market analysis. One of those areas is the use of deep learning models, i.e., Long Short-Term Memory (LSTM) networks, for time-series cryptocurrency forecasting.