Smart contracts are programs that reside and execute on a blockchain, like any transaction. They are automatically executed when preprogrammed terms and conditions are met. Although the smart contract (SC) must be presented in the blockchain for the integrity of data and transactions stored within it, it is highly exposed to several vulnerabilities attackers exploit to access the data. In this paper, classification and detection of vulnerabilities targeting smart contracts are performed using deep learning algorithms over two datasets containing 12,253 smart contracts. These contracts are converted into RGB and Grayscale images and then inserted into Residual Network (ResNet50), Visual Geometry Group-19 (VGG19), Dense Convolutional Network (DenseNet201), k-nearest Neighbors (KNN), and Random Forest (RF) algorithms for binary and multi-label classification. A comprehensive analysis is conducted to detect and classify vulnerabilities using different performance metrics. The performance of these algorithms was outstanding, accurately classifying vulnerabilities with high F1 scores and accuracy rates. For binary classification, RF emerged in RGB images as the best algorithm based on the highest F1 score of 86.66% and accuracy of 86.66%. Moving on to multi-label classification, VGG19 stood out in RGB images as the standout algorithm, achieving an impressive accuracy of 89.14% and an F1 score of 85.87%. To the best of our knowledge, and according to the available literature, this study is the first to investigate binary classification of vulnerabilities targeting Ethereum smart contracts, and the experimental results of the proposed methodology for multi-label vulnerability classification outperform existing literature.
In recent years, a more advanced form of phishing has arisen on Ethereum, surpassing early-stage, simple transaction phishing.This new form, which we refer to as payload-based transaction phishing (PTXPHISH), manipulates smart contract interactions through the execution of malicious payloads to deceive users.PTXPHISH has rapidly emerged as a significant threat, leading to incidents that caused losses exceeding $70 million in 2023 reports.Despite its substantial impact, no previous studies have systematically explored PTXPHISH.In this paper, we present the first comprehensive study of the PTXPHISH on Ethereum.Firstly, we conduct a long-term data collection and put considerable effort into establishing the first ground-truth PTXPHISH dataset, consisting of 5,000 phishing transactions.Based on the dataset, we dissect PTXPHISH, categorizing phishing tactics into four primary categories and eleven sub-categories.Secondly, we propose a rule-based multidimensional detection approach to identify PTXPHISH, achieving an F1-score of over 99% and processing each block in an average of 390 ms.Finally, we conduct a large-scale detection spanning 300 days and discover a total of 130,637 phishing transactions on Ethereum, resulting in losses exceeding $341.9 million.Our in-depth analysis of these phishing transactions yielded valuable and insightful findings.Scammers consume approximately 13.4 ETH daily, which accounts for 12.5% of the total Ethereum gas, to propagate address poisoning scams.Additionally, our analysis reveals patterns in the cash-out process employed by phishing scammers, and we find that the top five phishing organizations are responsible for 40.7% of all losses.Furthermore, our work has made significant contributions to mitigating real-world threats.We have reported 1,726 phishing addresses to the community, accounting for 42.7% of total community contributions during the same period.Additionally, we have sent 2,539 on-chain alert messages, assisting 1,980 victims.This research serves as a valuable reference in combating the emerging PTXPHISH and safeguarding users' assets.
Alexandre Pacheco, Sébastien De Vos, Andreagiovanni Reina, Marco Dorigo · 5 authors
Federated learning is a new approach to distributed machine learning that offers potential advantages such as reducing communication requirements and distributing the costs of training algorithms. Therefore, it could hold great promise in swarm robotics applications. However, federated learning usually requires a centralized server for the aggregation of the models. In this paper, we present a proof-of-concept implementation of federated learning in a robot swarm that does not compromise decentralization. To do so, we use blockchain technology to enable our robot swarm to securely synchronize a shared model that is the aggregation of the individual models without relying on a central server. We then show that introducing a single malfunctioning robot can, however, heavily disrupt the training process. To prevent such situations, we devise protection mechanisms that are implemented through secure and tamper-proof blockchain smart contracts. Our experiments are conducted in ARGoS, a physics-based simulator for swarm robotics, using the Ethereum blockchain protocol which is executed by each simulated robot.
Invariants are essential for ensuring the security and correctness of Solidity smart contracts, particularly in the context of blockchain's immutability and decentralized execution. This paper introduces InvSol, a novel framework for pre-deployment invariant generation tailored specifically for Solidity smart contracts. Unlike existing solutions, namely InvCon, InvCon+, and Trace2Inv, that rely on post-deployment transaction histories on Ethereum mainnet, InvSol identifies invariants before deployment and offers comprehensive coverage of Solidity language constructs, including loops. Additionally, InvSol incorporates custom templates to effectively prevent critical issues such as reentrancy, out-of-gas errors, and exceptions during invariant generation. We rigorously evaluate InvSol using a benchmark set of smart contracts and compare its performance with state-of-the-art solutions. Our findings reveal that InvSol significantly outperforms these tools, demonstrating its effectiveness in handling new contracts with limited transaction histories. Notably, InvSol achieves a 15% improvement in identifying common vulnerabilities compared to InvCon+ and is able to address certain crucial vulnerabilities using specific invariant templates, better than Trace2Inv.
not-yet-known not-yet-known not-yet-known unknown This paper investigates the current landscape of option trading platforms for cryptocurrencies, encompassing both centralized and decentralized exchanges. Option contracts in cryptocurrency markets offer functionalities akin to traditional markets, providing investors with tools to mitigate risks, particularly those arising from price volatility, while also allowing them to capitalize on future volatility trends. The paper discusses these applications of option contracts in the context of decentralized finance (DeFi), emphasizing their utility in managing market uncertainties. Despite a recent surge in the trading volume of option contracts on cryptocurrencies, decentralized platforms account for less than 1% of this total volume. Hence, this paper takes a closer look by examining the design choices of these platforms to understand the challenges hindering their growth and adoption. It identifies technical, financial, and adoption-related challenges that decentralized exchanges face and provides commentary on existing platform responses. Subsequently, it introduces a zero-loss liquidity provision strategy on altcoins that utilizes options with automated market makers. These opportunities result in a positive return with no significant risk. It then investigates opportunities in the past using historical on-chain data to emphasize the number of risk-free opportunities that DeFi is missing due to the lack of a functional options exchange on arbitrary ERC20 token pairs on Ethereum. The experiments show 1015 profitable instances in the past three years on 14 token pairs.
The article analyzes the potential of blockchain technology in the context of the global economy and digital trade.The authors explore how blockchain impacts various business processes, including financial operations, logistics, government administration, and environmental sustainability.Blockchain is becoming a key tool in financial transactions, ensuring transparency, security, and data immutability.These qualities help minimize fraud risks and provide reliable information protection.Due to its decentralized nature, blockchain can transform traditional supply chain management models, offering traceability at every stage and increasing trust in brands.This is particularly important in digital trade, where the involvement of numerous participants can complicate product tracking and quality control.An example of blockchain in supply chains is its implementation by Walmart for tracking food products.Blockchain significantly reduces the time required to verify the source of a product, ensuring a high level of transparency and safety at all stages of supply.Another example is the TradeLens platform, a joint project between Maersk and IBM, which uses blockchain to automate customs procedures and track containers worldwide.This technology helps reduce costs, avoid delays, and ensure the smooth flow of international trade.In government administration, blockchain is also widely applicable.It can increase the efficiency of government processes, ensure transparent elections, manage public registries, and allocate resources.Many countries are already experimenting with blockchain for elections, preventing fraud and ensuring secure voting.Additionally, the technology helps manage public registries, such as land ownership records, business licenses, or medical records, greatly improving data reliability and simplifying access to information.One of the key areas of blockchain development is decentralized finance (DeFi), which provides financial services without the involvement of traditional banks or financial institutions.DeFi opens up new opportunities for users, particularly in regions with weak banking infrastructure, contributing to financial inclusion.Decentralized finance offers a wide range of services such as lending, 6(6) 2024 226 insurance, and currency exchange, reducing the cost of financial operations and increasing service accessibility.Blockchain also holds great potential for optimizing supply chain management.For example, the VeChain platform specializes in tracking products in supply chains, ensuring transparency and control at every stage of production and delivery.This is particularly important for industries such as pharmaceuticals, food, and automotive, where product quality is critical.VeChain uses unique identifiers to record information about product origin, storage conditions, and transportation, ensuring trust in brands and minimizing the risk of counterfeits.Despite its advantages, blockchain technology faces challenges.One of the main issues is the low scalability of existing blockchain networks.Many popular platforms, such as Bitcoin and Ethereum, struggle with processing large volumes of transactions, leading to delays and increased fees, particularly during peak times.To address this problem, developers are working on new consensus protocols like Proof-of-Stake, which are more energy-efficient and capable of handling larger transaction volumes.Another serious issue is legal uncertainty related to the lack of unified international blockchain regulations.Different countries have different approaches to regulating the technology, creating legal risks for companies implementing blockchain in their business processes.For instance, some countries prohibit the use of cryptocurrencies or impose strict restrictions on their use, creating additional difficulties for international companies.Overall, blockchain has significant potential to transform the global economy, but to fully unlock its potential, several challenges related to legal uncertainty, scalability, and network energy efficiency need to be addressed.
With the rapid development of Zero-Knowledge Proofs (ZKPs), particularly Succinct Non-Interactive Arguments of Knowledge (SNARKs), benchmarking various ZK tools has become a valuable task. ZK-friendly hash functions, as key algorithms in blockchain, have garnered significant attention. Therefore, comprehensive benchmarking and evaluations of these evolving algorithms in ZK circuits present both promising opportunities and challenges. Additionally, we focus on a popular ZKP application, privacy-preserving transaction protocols, aiming to leverage SNARKs' cost-efficiency through "batch processing" to address high on-chain costs and compliance issues. To this end, we benchmarked three SNARK proving systems and five ZK-friendly hash functions, including our self-developed circuit templates for Poseidon2, Neptune, and GMiMC, on the bn254 curve within the circom-snarkjs framework. We also introduced the role of "sequencer" in our SNARK-based privacy-preserving transaction scheme to enhance efficiency and enable flexible auditing. We conducted privacy and security analyses, as well as implementation and evaluation on Ethereum Virtual Machine (EVM)-compatible chains. The results indicate that Poseidon and Poseidon2 demonstrate superior memory usage and runtime during proof generation under Groth16. Moreover, compared to the baseline, Poseidon2 not only generates proofs faster but also reduces on-chain costs by 73% on EVM chains and nearly 26% on Hedera. Our work provides a benchmark for ZK-friendly hash functions and ZK tools, while also exploring cost efficiency and compliance in ZKP-based privacy-preserving transaction protocols.
Ethereum is a distributed, peer-to-peer blockchain infrastructure that has attracted billions of dollars. Perhaps due to its success, Ethereum has become a target for various kinds of attacks, motivating researchers to explore different techniques to identify vulnerabilities in EVM bytecode (the language of the Ethereum Virtual Machine)—including formal verification, symbolic execution, and fuzz testing. Although recent studies empirically compare smart contract fuzzers, there is a lack of literature investigating how simpler grey-box fuzzers compare to more advanced ones. To fill this gap, in this paper, we present DogeFuzz, an extensible infrastructure for fuzzing Ethereum smart contracts, currently supporting black-box fuzzing and two grey-box fuzzing strategies: coverage-guided grey-box fuzzing (DogeFuzz-G) and directed grey-box fuzzing (DogeFuzz-DG). We conduct a series of experiments using benchmarks already available in the literature and compare the DogeFuzz strategies with state-of-the-art fuzzers for smart contracts. Surprisingly, although DogeFuzz does not leverage advanced techniques for improving input generation (such as symbolic execution or machine learning), DogeFuzz outperforms sFuzz and ILF, two state-of-the-art fuzzers. Nonetheless, the Smartian fuzzer shows higher code coverage and bug-finding capabilities than DogeFuzz.
The advent of healthcare delivery drones, a novel unmanned aerial vehicle (UAV), has opened the door to a more comprehensive approach to developing smart health services. Internet of Things (IoT)-based healthcare delivery via UAVs has not been fully realized because of a lack of augmentation-aware concepts. Despite community pharmacists' strong clinical expertise and ease of patient access, they cannot provide comprehensive clinical services due to a lack of access to patient data. Blockchain could be a solution if it's concerned about the reliability and security of drone deliveries over untrusted open channels. Hence, this study proposes the Internet of Things and Blockchain Technology-assisted Drone Healthcare Delivery System (IoT-BT-DHDS) for enhancing access to healthcare information in the community pharmacy environment. Ensuring the security of drone operations is crucial to protect users from any breaches that might result in physical and financial loss. This research suggests an oT-BT-DHDS that authenticates and registers the involved entities, such as objects (healthcare supplies), warehouses (healthcare centres), and drones, to address these security concerns and make the delivery progression visible. This research achieves its goals by analyzing several parameters that impact the time and number of transactions required for authentication on the Ethereum platform, which is used to build smart contracts and blockchain. The suggested strategy is shown to be effective by results acquired from a simulated environment compared to alternative models based on an access control ratio of 98.9%, data management ratio of 97.4% and resource utilization ratio of 96.4% to other existing models.
Consumers and brands are at serious risk due to the growth of counterfeit goods, especially in regions like Nigeria. Conventional techniques, such border inspections and market raids by the Standards Organization of Nigeria (SON), are inadequate for detecting counterfeit goods. To ensure traceability, transparency, and immutability in the supply chain, this article suggests utilizing blockchain technology. The decentralized and encrypted characteristics of blockchain, when bolstered by smart contracts, enable efficient product tracking from producers to end users, hence impeding the infiltration of fake goods. Using a permissioned blockchain network, this system attempts to confirm the legitimacy of products at every point along the supply chain—manufacturers, distributors, retailers, and end users. The Remix IDE is used to deploy and test Ethereum-based smart contracts that were created in Solidity for the proposed system. This blockchain-based strategy aims to decrease the spread of counterfeit goods, protect consumer confidence, and preserve brand reputation. To offer a user-friendly interface for wider accessibility, future advancements will link this system with decentralized apps (DApps).
One of the most widely used technologies for peer-to-peer real-time data transmission is WebRTC (Web Real-Time Communication).Both in the military and in civilian life, WebRTC technology is employed in many background applications, either directly or indirectly.For this reason, WebRTC communication security is crucial.The main goal of this research is to enhance WebRTC's current security framework by utilizing relatively new and smart contract technologies, the suggested solution offers a real-time system for communication amongst peers on the network.Additionally, a web application featuring a video calling function that illustrates blockchain interactions as well as recommended security improvements has been built.Furthermore, the application may be used on the smart contract that is active on the Ethereum network to carry out tasks like declaring, modifying, and authorizing users.As a result, this article suggests a safe identity verification technique that peers for real-time WebRTC communication could utilize to validate one another on the blockchain.
The emergence of cryptocurrencies as a form of digital payments has contributed to the emergence of numerous opportunities for the implementation of effective and efficient financial transactions, however, new fraud and money laundering schemes have emerged, as the anonymity and decentralization inherent in cryptocurrencies complicate the process of monitoring transactions and control by governments and law enforcement agencies. This study aims to develop a mechanism for analyzing transactions in the Ethereum cryptocurrency using a Bayesian classifier to identify potentially suspicious transactions that may be related to terrorist financing and money laundering. The Bayesian approach makes it possible to consider the probabilistic characteristics of transactions and their interrelationships to increase the accuracy of detecting anomalous and potentially illegal transactions. For the analysis, data on transactions of the Ethereum currency from June 2020 to December 2022 were taken. The developed mechanism involves determining a set of characteristics of transaction graph nodes that identify the potential for their use in illegal financial transactions and forming intervals of their permissible values. The article presents cryptocurrency transactions as an oriented graph, with the nodes being the entities conducting transactions and the arcs being the transactions between the nodes. In assessing the risks of using cryptocurrencies in money laundering, the number/amount of transactions to and from the respective node, the balance of these transactions (absolute value), and the type of node were considered. The analysis showed that among the 100 largest nodes in the network, 11 were identified as having a «critical» risk level, and the most closely connected nodes were identified. This methodology can be used not only to analyze the Ethereum cryptocurrency but also for other cryptocurrencies and similar networks.
Paulo César Galarza-Sánchez, Gerardo Alfredo Solano Gutiérrez
La globalización y la complejidad de las cadenas de suministro han destacado la necesidad de soluciones tecnológicas que mejoren la trazabilidad, transparencia y eficiencia. Este estudio tiene como objetivo comparar las principales plataformas blockchain aplicadas en la gestión de cadenas de suministro, explorando sus características, ventajas, limitaciones y casos de uso. A través de una metodología de análisis bibliográfico, se revisaron fuentes académicas de bases de datos reconocidas, utilizando palabras clave relevantes y criterios estrictos de inclusión y exclusión. Los resultados muestran que plataformas privadas, como Hyperledger Fabric, ofrecen mayor escalabilidad y control de acceso, mientras que plataformas públicas, como Ethereum, priorizan la transparencia, aunque presentan limitaciones en términos de velocidad y costos. Además, la integración de blockchain mejora la trazabilidad y transparencia en las cadenas, permitiendo un seguimiento en tiempo real y fomentando la confianza entre los actores. Sin embargo, factores como la falta de interoperabilidad, los altos costos iniciales y la resistencia al cambio organizacional limitan su adopción. En conclusión, blockchain puede transformar las cadenas de suministro, pero su implementación requiere superar barreras tecnológicas y culturales mediante estrategias colaborativas, estándares comunes y formación.
The integration of blockchain technology and cryptocurrency within the framework of Islamic finance has raised significant ethical, legal, and regulatory concerns. Blockchain technology, known for its transparency, decentralization, and immutability, offers a promising solution for enhancing financial inclusion, transparency, and security in financial transactions. However, the use of cryptocurrencies, such as Bitcoin and Ethereum, introduces complexities due to their speculative nature, which may violate Sharia principles like gharar (excessive uncertainty) and riba (usury). This study explores the compatibility of blockchain and cryptocurrency with Sharia law, focusing on the challenges and opportunities that arise in the context of Islamic finance. The study analyzes existing fatwas (Islamic legal opinions), regulatory frameworks, and the application of Sharia principles to emerging financial technologies. It discusses the ethical dimensions of blockchain and cryptocurrency, such as their potential to promote fairness and transparency, while addressing concerns about privacy violations and the risks associated with unregulated trading. Furthermore, the research highlights the lack of standardized global regulations for cryptocurrency and blockchain, which complicates their adoption in Muslim-majority countries. The study also emphasizes the importance of establishing Sharia-compliant governance frameworks and regulatory standards to ensure the ethical use of these technologies. Finally, the study provides recommendations for further research in the intersection of Islamic law, digital finance, and global governance frameworks, focusing on the development of policies that ensure Sharia-compliant digital assets and technologies.
Mahdi Akbari Zarkesh, Ehsan Dastani, Bardia Safaei, Ali Movaghar
The pervasive adoption of Internet of Things (IoT) has significantly advanced healthcare digitization and modernization. Nevertheless, the sensitive nature of medical data presents security and privacy challenges. On the other hand, resource constraints of IoT devices often necessitates cloud services for data handling, introducing single points of failure, processing delays, and security vulnerabilities. Meanwhile, the blockchain technology offers potential solutions for enhancing security, decentralization, and data ownership. An ideal solution should ensure confidentiality, access control, and data integrity while being scalable, cost-effective, and integrable with the existing systems. However, current blockchain-based studies only address some of these requirements. Accordingly, this paper proposes EdgeLinker; a comprehensive solution incorporating Proof-of-Authority consensus, integrating smart contracts on the Ethereum blockchain for access control, and advanced cryptographic algorithms for secure data communication between IoT edge devices and the fog layer in healthcare fog applications. This novel framework has been implemented in a real-world fog testbed, using COTS fog devices. Based on a comprehensive set of evaluations, EdgeLinker demonstrates significant improvements in security and privacy with reasonable costs, making it an affordable and practical system for healthcare fog applications. Compared with the state-of-the-art, without significant changes in the write-time to the blockchain, EdgeLinker achieves a 35% improvement in data read time. Additionally, it is able to provide better throughput in both reading and writing transactions compared to the existing studies. EdgeLinker has been also examined in terms of energy, resource consumption and channel latency in both secure and non-secure modes, which has shown remarkable improvements.
Aims This study will investigate the integration of quantum computing and blockchain technology of EHR systems, evaluating the potential and major vulnerabilities of the developed blockchain platforms. In addition, through this evaluation, in this paper, transaction capabilities, energy consumption, and quantum susceptibilities of Ethereum, Bitcoin, and Ripple are being evaluated. Further, research gaps on quantum implications and transition strategies to quantum-resistant systems for achieving secure, efficient, and patient-centric Healthcare 4.0 are identified. Background The embedding of quantum computing and blockchain technology within EHR systems represents the next wave of scientific development within the healthcare sector. However, at the same time, emerging quantum capabilities have raised serious vulnerabilities for major blockchain platforms. If Ethereum and Bitcoin display quantum threats regarding their high transaction capacities, then Ripple, with its high rate of transactions, truly presents a high stake in terms of quantum threats. Further, the energy consumption discrepancies pose some environmental impacts and point to the need for research on energy-efficient quantum-resistant systems. Objective This research investigates the potential and vulnerabilities of major blockchain platforms with electronic health record systems in a new quantum computing environment. In that context, this work evaluates transaction capacities, quantum threats, and energy use for platforms like Ethereum, Bitcoin, and Ripple. Additionally, it seeks to identify research gaps and propose transition strategies toward a quantum-resistant system in support of the development of a secure and efficient Healthcare 4.0. Methods This work focused on assessing the potential and vulnerabilities of blockchain platforms under quantum computing threats in EHR systems. We analyzed transaction processing rates, quantum susceptibilities, and energy consumption metrics for the Ethereum, Bitcoin, and Ripple platforms. A complete literature review is presented with respect to realistic quantum implications and practical transition strategies toward quantum-resistant systems oriented to support the development of secure and efficient Healthcare 4.0. Results The evaluations revealed that Ethereum processed 30 transactions per second and Bitcoin processed 7, with each having low quantum vulnerability. Ripple, at 1500 transactions per second, also had significant quantum vulnerabilities. In addition to energy use, Bitcoin consumes 707 kWh per single transaction compared with Ripple's 0.0078 kWh. Other gaps in research existed in real-world quantum consequences and considerations for transitioning to quantum-resistant systems, all of which are vital for making Healthcare 4.0 secure and efficient. Conclusion This has underscored the transformative potential as well as the weaknesses involved in integrating quantum computing and blockchain technologies into EHR. However, Ethereum, Bitcoin, and Ripple vary in their transaction rates; all three face a similar quantum threat while having large differences in energy consumption. These are problems that would call for more research into quantum-resistant systems and strategic implementation. Actualization of a secure, efficient, and patient-centered Healthcare 4.0 will call for proactive research collaboration and strategic efforts towards ensuring technological and environmental sustainability.
Aparna Vijayakumar, S. V. Annlin Jeba, Aneetta Ann Mathew, Megha Ann Raju · 5 authors
The healthcare sector is confronting a number of difficulties, including the vulnerability of sensitive patient data, privacy breaches, and medical record theft. Healthcare data is particularly sensitive and requires fail-safe measures to avoid unauthorized access, leakage, and modification. The existing environment is riddled with errors, interoperability challenges, complicated compliance laws, and poor data sharing procedures, all of which dramatically increase the danger of data theft. Recognizing the crucial need for revolutionary action, a paradigm change is advocated for healthcare data security. The suggested approach seeks not only to prevent fraud, but also to build a framework for assuring the absolute integrity and validity of shared patient data. This unique technology goes beyond security by providing the secure transmission of critical health information with access restricted to authorized persons. Adopting a blockchain architecture, like as Ethereum, creates a decentralized and tamper-resistant ledger that allows for the transparent and safe recording of patient-doctor interactions.
This article proposes a new digital watermarking mechanism based on the Ethereum blockchain, Smart Contract, and Interplanetary File System (IPFS), with an enhanced Fast Walsh Hadamard Transform (FWHT) algorithm for watermark embedding and extraction. The proposed scheme aims to address the limitations of existing digital watermarking techniques, such as dependence on third-party platforms, by leveraging the decentralization feature of blockchain. The Smart Contract is used to manage the transaction between the parties involved in the watermarking process, while IPFS is used to store the watermark data. The enhanced FWHT algorithm is used to embed the watermark into the host image without affecting its visual quality. The results show that the proposed scheme outperforms the state-of-the-art algorithms in terms of both imperceptibility and robustness. Additionally, it demonstrates that our scheme can effectively resist various attacks. Therefore, our scheme can be a promising solution for image copyright protection, authentication applications, and image trading.
Open access
Advanced Steganography and Watermarking Techniques
Κωνσταντίνος Γκίλλας, Maria Tantoula, Manolis Tzagarakis
Abstract We analyze properties identified in the price volatility of Bitcoin and some of the leading cryptocurrencies namely Litecoin, Ripple, and Ethereum. We employ Heterogeneous Autoregressive models (HAR) in both a univariate and multivariate level of analysis. First, the significance of heterogeneity and jumps is examined, considering the ability of several univariate HAR models, to predict realized volatility of cryptocurrencies. Second, we examine the relevance of realized volatility jumps and covariances in the transmission of volatility spillovers among cryptocurrencies. We perform a comparative spillover analysis of the multivariate HAR models in two versions, considering variances only and covariances as well. Our results indicate that covariances and jumps inclusion lead to an increase in spillovers. The time-varying spillover analysis indicates higher dependency between Bitcoin and the other cryptocurrencies mostly at short frequencies.
Burhan Ul Islam Khan, Khang Wen Goh, Abdul Raouf Khan, Megat F. Zuhairi · 5 authors
Blockchain is recognized for its robust security features, and its integration with Internet of Things (IoT) systems presents scalability and operational challenges. Deploying Artificial Intelligence (AI) within blockchain environments raises concerns about balancing rigorous security requirements with computational efficiency. The prime motivation resides in integrating AI with blockchain to strengthen IoT security and withstand multiple variants of lethal threats. With the increasing number of IoT devices, there has also been a spontaneous increase in security vulnerabilities. While conventional security methods are inadequate for the diversification of IoT devices, adopting AI can assist in identifying and mitigating such threats in real time, whereas integrating AI with blockchain can offer more intelligent decentralized security measures. The paper contributes to a three-layered architecture encompassing the device/sensory, edge, and cloud layers. This structure supports a novel method for assessing legitimacy scores and serves as an initial security measure. The proposed scheme also enhances the architecture by introducing an Ethereum-based data repositioning framework as a potential trapdoor function, ensuring maximal secrecy. To complement this, a simplified consensus module generates a conclusive evidence matrix, bolstering accountability. The model also incorporates an innovative AI-based security optimization utilizing an unconventional neural network model that operates faster and is enhanced with metaheuristic algorithms. Comparative benchmarks demonstrate that our approach results in a 48.5% improvement in threat detection accuracy and a 23.5% reduction in processing time relative to existing systems, marking significant advancements in IoT security for smart cities.
In the realm of smart contract security, transaction malice detection has been able to leverage properties of transaction traces to identify hacks with high accuracy. However, these methods cannot be applied in real-time to revert malicious transactions. Instead, smart contracts are often instrumented with some safety properties to enhance their security. However, these instrumentable safety properties are limited and fail to block certain types of hacks such as those which exploit read-only re-entrancy. This limitation primarily stems from the Ethereum Virtual Machine's (EVM) inability to allow a smart contract to read transaction traces in real-time. Additionally, these instrumentable safety properties can be gas-intensive, rendering them impractical for on-the-fly validation. To address these challenges, we propose modifications to both the EVM and Ethereum clients, enabling smart contracts to validate these transaction trace properties in real-time without affecting traditional EVM execution. We also use past-time linear temporal logic (PLTL) to formalize transaction trace properties, showcasing that most existing detection metrics can be expressed using PLTL. We also discuss the potential implications of our proposed modifications, emphasizing their capacity to significantly enhance smart contract security.
Олександр Кузнецов, Anton Yezhov, Kateryna Kuznetsova, Oleksandr Domin
This study presents a comprehensive theoretical and empirical analysis of Patricia tries, the fundamental data structure underlying Ethereum's state management system. We develop a probabilistic model characterizing the distribution of path lengths in Patricia tries containing random Ethereum addresses and validate this model through extensive computational experiments. Our findings reveal the logarithmic scaling of average path lengths with respect to the number of addresses, confirming a crucial property for Ethereum's scalability. The study demonstrates high precision in predicting average path lengths, with discrepancies between theoretical and experimental results not exceeding 0.01 across tested scales from 100 to 100,000 addresses. We identify and verify the right-skewed nature of path length distributions, providing insights into worst-case scenarios and informing optimization strategies. Statistical analysis, including chi-square goodness-of-fit tests, strongly supports the model's accuracy. The research offers structural insights into node concentration at specific trie levels, suggesting avenues for optimizing storage and retrieval mechanisms. These findings contribute to a deeper understanding of Ethereum's fundamental data structures and provide a solid foundation for future optimizations. The study concludes by outlining potential directions for future research, including investigations into extreme-scale behavior, dynamic trie performance, and the applicability of the model to non-uniform address distributions and other blockchain systems.
Manuel J. Fernández Iglesias, Christian Delgado‐von‐Eitzen, Luis Anido
The growing importance of traceability in supply chains requires robust, transparent, and efficient systems to ensure the integrity and authenticity of product journeys. This paper presents a comprehensive characterisation and data model for a generic blockchain-based traceability system, highlighting its implementation using smart contracts on Ethereum-compatible networks, as the Ethereum Virtual Machine (EVM), with its pioneering implementation of smart contracts and its extensive ecosystem; it provides a robust environment for developing decentralised applications. We discuss the advantages of using blockchain technology to notarise traceability activities, ensuring immutability and transparency by exploring two main scenarios, namely one where hash keys (i.e, cryptographic digests) are stored on-chain while detailed data remain off-chain, and another where all traceability data are fully stored on-chain. Each approach is evaluated for its impact on scalability, privacy, storage efficiency, and operational costs. The hash key method offers significant advantages in reducing blockchain storage costs, enhancing privacy, and maintaining data integrity, but it depends on reliable off-chain storage. Conversely, the full on-chain approach guarantees data immutability but at a higher cost and lower scalability. By combining these strategies, a balanced solution can be achieved, leveraging the strengths of both methods to provide a reliable, efficient, and secure blockchain-based traceability system, which is illustrated with a practical implementation to support traceability in the timber sector in Galicia, Spain. This paper aims to provide valuable insights for researchers and practitioners looking to implement or enhance traceability systems using blockchain technology, demonstrating how smart contracts can be effectively utilised to meet the demanding requirements of modern supply chains.