Vladyslav Nekriach, Sidi Mohamed Beillahi, C. Li, Peilun Li · 7 authors
This paper introduces HEMVM, an innovative heterogeneous blockchain framework that seamlessly integrates diverse virtual machines (VMs), including the Ethereum Virtual Machine (EVM) and the Move Virtual Machine (MoveVM), into a unified system. This integration facilitates interoperability while retaining compatibility with existing Ethereum and Move toolchains by preserving high-level language constructs. HEMVM's unique cross-VM operations allow users to interact with contracts across various VMs using any wallet software, effectively resolving the fragmentation in user experience caused by differing VM designs. Our experimental results demonstrate that HEMVM is both fast and efficient, incurring minimal overhead (less than 4.4 %) for intra-VM transactions and achieving up to 9300 TPS for cross-VM transactions. Our results also show that the cross-VM operations in HEMVM are sufficiently expressive to support complex decentralized finance interactions across multiple VMs. Finally, the parallelized prototype of HEMVM shows performance improvements up to 44.8 % compared to the sequential version of HEMVM under workloads with mixed transaction types.
W. M. A. B. Wijesundara, Joong-Sun Lee, Eleni Aloupogianni, Dara Tith · 6 authors
Rapid proliferation of smart home IoT devices has intensified the demand for secure, scalable, and autonomous firmware authentication mechanisms. Traditional centralized solutions face challenges related to privacy concerns, limited scalability, and vulnerability to single point of failure. In this paper, we propose DIDAuth-IoTFW, a novel decentralized identity and firmware authentication framework that uniquely integrates Ethereum Layer-2 Arbitrum, InterPlanetary File System (IPFS), and W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). DIDAuth-IoTFW provides a complete firmware authentication life cycle, from decentralized identity registration to real-time, on-chain verifiable revocation. While enabling autonomous, cryptographic verification directly on resource-constrained IoT devices and ensuring reliable performance even when gateways are compromised or unavailable. Our proof-of-concept implementation on ESP32 and Raspberry Pi achieved complete resistance to replay, forgery, and revocation threats with verification consistently under 1.2 s. Compared to prior work, DIDAuth-IoTFW uniquely combines firmware–VC hash binding, contract binding that prevents cross-registry replay, and device-side enforcement resilient to gateway compromise. Experimental results indicate a robust, privacy-preserving, and scalable alternative to centralized firmware-update pipelines for smart-home IoT.
The Blockchain algorithm has advanced the accountability and transparency of modern digital infrastructures. Enforcing responsible behavior and data integrity across distributed environments involves several key components, such as smart contracts, access control models, cryptographic techniques, and a decentralized identity framework. Because the blockchain ledger is immutable and transparent, once a transaction is recorded, it cannot be altered without detection, making fraudulent actions easily traceable and thereby ensuring accountability. However, the need for hybrid approaches that combine on-chain and off-chain solutions for an efficient reliability system introduces challenges, including privacy preservation, scalability, and regulatory compliance. This paper analyzes the effective features that enhance blockchain accountability, such as immutability, traceability, auditability, and decentralized control. We propose research gap directions for the research community. To improve the reliability of blockchain systems across various domains, based on a systematic analysis and integration of recent developments and real-world demands. Consequently, we have distinguished 33 relevant research studies from a total of 358 publications covering the period between 2020 and 2025 by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. We identified three major themes addressed by the papers in the reviewed studies: further investigations into the ML role in enhancing accountability are required, especially using lightweight ML algorithms such as BNN and Tseltin machine, examining the limitations of blockchain’s auditability for real-time applications and decision-making efficiency, and a practical study of mechanism scalability in trade-off cost-efficiency.
Fuzzing is an effective technique to detect vulnerabilities in smart contracts. The challenge of smart contract fuzzing lies in the statefulness of contracts, which indicates that certain vulnerabilities can only be manifested in specific contract states. State-of-the-art fuzzers may generate and execute a plethora of meaningless or redundant transaction sequences during fuzzing, incurring a penalty in efficiency. To this end, we present DepFuzz , a hybrid fuzzer for efficient smart contract fuzzing, which introduces a symbolic execution module into the feedback-based fuzzer. Guided by the distance-based function dependencies between functions, DepFuzz can efficiently yield meaningful transaction sequences that contribute to vulnerability exposure or code coverage. The experiments on 286 benchmark smart contracts and 500 large real-world smart contracts corroborate that, compared to state-of-the-art approaches, DepFuzz achieves higher instruction coverage rate and uncovers many more vulnerabilities with less time.
Open access
Blockchain Technology Applications and Security
Auction Theory and Applications
Advanced Steganography and Watermarking Techniques
Climate change, energy crises, military actions in the world, and unstable oil prices create enormous challenges for the world’s nations. The need to use less fossil fuels opens opportunities for new or somewhat neglected green technologies. To reform the energy sector, it is necessary to have a clear and measured strategy. This allows for the identification of the potential of renewable resources in each space, while finding sources of financing for the transformation. The use of renewable resources makes it possible to solve an actual problem of the developed world—as the population of cities grows, the economic vitality of regions drops significantly. By creating new energy production capacities in the regions, social exclusion is reduced, and the main resources of the regions are better used—land areas, farms, and biomass sources. Nowadays, mankind is experiencing the third significant transformation that converts from conventional fossil fuels to new energy. The future development will go along with the three major trends—resource-type carbon reduction, production technology intensification, and utilization method diversification [1]. Based on these directions of transformation, the main investment decisions will be made, which will promote the progress of the energy system. Humanity is so far the least advanced in carbon collection and burial technologies, but the development of other trends allows for tangible progress.
This study investigates the safe-haven and diversification roles of gold and Bitcoin in financial markets from 2015 to 2025. With rising economic uncertainties, the need for reliable safe-haven assets has become critical. Gold has historically provided stability during crises, whereas Bitcoin's volatile nature raises questions about its reliability as a safe haven. Through empirical analysis, including GARCH models and quantile regression, the research evaluates the performance of both assets during market stress. Findings indicate that gold consistently outperforms Bitcoin in terms of downside protection, confirming its status as a traditional safe-haven asset. Conversely, Bitcoin offers potential diversification benefits, enhancing portfolio performance when combined with gold. The results support the notion that integrating both assets can lead to improved risk-adjusted returns, making a dual-asset investment strategy a practical approach for investors navigating market uncertainties.
The rapid adoption of Internet of Things (IoT) in Healthcare has significantly enhanced real-time patient monitoring and decision making. However, security and privacy still remain the major concern due to sensitive medical data of patients especially on low-power IoT devices. Traditional authentication schemes like Zero Knowledge Proof (ZKP) and Elliptic Curve Cryptography (ECC) often struggle with efficiency in resource-constraint environments due to their computational overhead. In order to address these challenges, we propose a Neural-Based Hybrid and Adaptive Framework that combines Schnorr ZKP with Kyber-based key encapsulation, using a neural network to dynamically select Kyber variants (512, 768, 1024) based on device parameters (type, authentication time, transmission time) to balance security and efficiency for low-power IoT devices. Extensive experiments validated robust security against replay and spoofing attacks, achieving authentication success for legitimate clients and zero attack successes. Furthermore, our proposed framework outperforms traditional Kyber1024 and ZKP/ECC based authentication schemes in terms of authentication time and computational overhead making it robust and scalable solution for sensitive and resource-limited environments like HealthCare IoT systems. • Design a novel hybrid authentication framework that integrates ZKP and Kyber PQC (Post Quantum Cryptography) to ensure secure and efficient authentication for IoT healthcare devices. • Utilize Neural Network to intelligently select the most appropriate Kyber variant based on threat level and computational efficiency. • Provide comprehensive experimental analysis comparing the proposed hybrid and adaptive framework with traditional ZKP, ECC and static Kyber implementations.
Reilly Smethurst, Orestis Papageorgiou, Ben Egliston
Blockchain-tokenised media is kitsch. We examined OpenSea’s top 50 collections of non-fungible tokens (NFTs) as well as the first two NFTs acquired by New York’s Museum of Modern Art (MoMA). We concomitantly developed two concepts: oppositional kitsch, and historically informed kitsch. OpenSea’s profile picture collections exemplify oppositional kitsch: they are described by their creators as anti-normal, degenerate or animalistic. CryptoPunks and Bored Apes are the best-known examples. Historically informed kitsch is, by contrast, pleasant and reassuring. MoMA’s first two NFTs exemplify historically informed kitsch: they are produced by artificial intelligence (AI) models that rely on historical data. For Unsupervised – Machine Hallucinations – MoMA , Refik Anadol trained an AI model with images from MoMA’s GitHub archive. The Unsupervised NFTs are mementos of individuals’ encounters with the work at MoMA. For 3FACE , Ian Cheng created an AI model that draws on blockchain transaction histories. The AI model generates tokenised portraits of individuals based on their transactions. Oppositional kitsch and historically informed kitsch both valorise the individual consumer. OpenSea’s oppositional kitsch lets consumers identify with cartoon figures like CryptoPunks and Bored Apes that are marketed as avatars (alter-egos) and profile pictures. MoMA’s historically informed kitsch sells mementos of individuals’ experiences and portraits that are based on individuals’ consumption choices. Our conception of blockchain-tokenised media as kitsch is primarily informed by the philosophers Alain Badiou and Thorsten Botz-Bornstein as well as the media theorist Jean Baudrillard. These three thinkers treat kitsch as a category of sophistic, liberal-cum-libertarian culture.
Jinho Cha, Young‐Chul Kim, Junyeol Ryu, Sangjun Park · 6 authors
This study develops a strategic procurement framework integrating blockchain-based smart contracts with bounded demand variability modeled through a truncated normal distribution. While existing research emphasizes the technical feasibility of smart contracts, the operational and economic implications of adoption under moderate uncertainty remain underexplored. We propose a multi-supplier model in which a centralized retailer jointly determines the optimal smart contract adoption intensity and supplier allocation decisions. The formulation endogenizes adoption costs, supplier digital readiness, and inventory penalties to capture realistic trade-offs among efficiency, sustainability, and profitability. Analytical results establish concavity and provide closed-form comparative statics for adoption thresholds and procurement quantities. Extensive numerical experiments demonstrate that moderate demand variability supports partial adoption strategies, whereas excessive investment in digital infrastructure can reduce overall profitability. Dynamic simulations further reveal how adaptive learning and declining implementation costs progressively enhance adoption intensity and supply chain performance. The findings provide theoretical and managerial insights for balancing digital transformation, resilience, and sustainability objectives in smart contract-enabled procurement.
Ziyang Liu, Kenneth MacKenzie, Roman Kireev, Michael Peyton Jones · 6 authors
The Cardano blockchain is the first to use proof of stake, offers native support for multiple currencies and is evolving toward a distributed governance model. It supports smart contracts through Plutus, a language based on System Fω with recursion. About half a dozen languages compile into Plutus, the first of which is Plinth (formerly Plutus Tx) — a language that reuses a subset of the Haskell syntax, and has been in commercial use since 2021.
Zero-knowledge proof (ZKP) applications require translating high-level programs into arithmetic circuits–a process that demands both correctness and efficiency. While recent DSLs improve usability, they often yield suboptimal circuits, and hand-optimized implementations remain difficult to construct and verify. We present Tabby, a synthesis-aided compiler that automates the generation of high-performance ZK circuits from highlevel code. Tabby introduces a domain-specific intermediate representation designed for symbolic reasoning and applies sketch-based program synthesis to derive optimized low-level implementations. By decomposing programs into reusable components and verifying semantic equivalence via SMT-based reasoning, Tabby ensures correctness while achieving substantial performance improvements. We evaluate Tabby on a suite of real-world ZKP applications and demonstrate significant reductions in proof generation time and circuit size against mainstream ZK compilers.
Abstract Artificial intelligence (AI) systems are rapidly approaching capabilities that require an increasing level of human control. Existing AI alignment techniques remain opaque, model-specific, and vulnerable in human-level AI, or post-quantum scenarios. To address these issues, this paper proposes a novel AI alignment system architecture in which AI alignment rules are encoded as immutable smart contracts on a blockchain. The blockchain, in turn, is governed by a Proof of Personhood (PoP) consensus mechanism that only admits human agents to the rule validation processes. To protect the privacy of human agents in the identity verification process, the proposed AI alignment system facilitates techniques such as key derivation functions and asymmetric encryption of biometric data. In addition, this system also utilizes blockchain-based decentralized identity (DID) and zero-knowledge proofs (ZKPs). To ensure privacy in post-quantum scenarios, biometric data are linked to zk-STARKs. The proposed AI alignment system is formally described to capture human and AI agents, verification, authentication, and Sybil resistance. The AI shield, a reactive system that prevents unsafe actions by an AI agent that would violate predetermined conditions, enforces the blockchain-based AI alignment rules in real-time, independently of the underlying AI model. Thus, the contribution of this paper is a conceptual framework for the implementation of blockchain technology that utilizes a PoP-based consensus mechanism and zk-STARKs to foster privacy-friendly societal involvement and public auditability of AI developments, providing a democratically governed AI alignment layer applicable to current and future AI models, including those in a post-quantum era.
Ejielo Ogbuefi, Stephen Ehilenomen Aifuwa, Jennifer Olatunde-Thorpe, David Akokodaripon
Resilience in critical infrastructures (CIs) has emerged as a pressing global concern, as societies become increasingly dependent on tightly interconnected systems that support communication, energy, finance, and healthcare. These infrastructures, once considered largely independent, now operate within a convergent ecosystem where disruptions in one domain can propagate rapidly across others, leading to cascading failures with severe societal and economic consequences. Conceptual frameworks addressing resilience in such interdependent systems emphasize the need to move beyond traditional robustness toward adaptive, absorptive, and restorative capacities that account for dynamic risks, systemic vulnerabilities, and emergent behaviors. The convergence of communication technologies with energy, financial, and healthcare services introduces both opportunities and challenges. While digital integration enhances efficiency, situational awareness, and service delivery, it also amplifies exposure to cyber threats, systemic shocks, and supply chain fragilities. For instance, a cyberattack on a power grid can simultaneously disrupt healthcare delivery and financial transactions, while communication failures may impede crisis coordination. To address these risks, resilience frameworks increasingly adopt network-centric, socio-technical, and complex adaptive systems perspectives, highlighting interdependencies and the need for multi-level governance. Key strategies include embedding redundancy and decentralization in communication systems, deploying microgrids and storage in energy infrastructures, integrating distributed ledgers and systemic risk monitoring in finance, and strengthening telemedicine and emergency preparedness in healthcare. At a broader level, resilience planning requires cross-sector interoperability standards, public–private collaboration, and ethical prioritization of vulnerable populations. Emerging tools such as AI-driven predictive analytics and digital twins offer promising avenues for resilience assessment and proactive adaptation. Ultimately, resilience in convergent critical infrastructures demands an integrated, multidisciplinary approach that bridges engineering, policy, and social dimensions. By adopting conceptual frameworks that embrace interdependency, adaptability, and inclusivity, societies can enhance preparedness, mitigate cascading risks, and ensure continuity of vital services under conditions of uncertainty.
Open access
Infrastructure Resilience and Vulnerability Analysis
Abdullah Yousafzai, Muhammad Mohsan Sheeraz, Ganna Pogrebna, Jon Crowcroft · 5 authors
The metaverse is a shared virtual 3D space that combines immersive experiences with applications in gaming, social interactions, commerce, and more. It is rapidly becoming a reality, driven by advances in virtual reality, augmented reality, artificial intelligence, blockchain, and other emerging technologies. Among these, blockchain technology enables secure and decentralized ownership as well as seamless interoperability of virtual assets. Non-fungible tokens ensure verifiable ownership and fraud prevention, while smart contracts facilitate automated peer-to-peer transactions. Blockchain’s security and transparency promote trust and innovation, laying the foundation for a connected and user-driven metaverse ecosystem. In this paper, we explore the role of blockchain technology as a key enabler for the metaverse, providing solutions for decentralization, governance through decentralized autonomous organizations, interoperable mechanisms, digital asset ownership, traceability, auditing, and identity management. We present the key difference between traditional virtual worlds and the metaverse, and why blockchain is preferred over other decentralized technologies for the metaverse. We comprehensively review recent advances in metaverse system architectures, focusing on state-of-the-art solutions and lessons learned. We compare the existing literature based on key parameters; namely, contributions, advantages, limitations, and applications. We present key challenges, including deepfake threats, identity theft and brand infringement risks, mental health risks, digital safety and gambling risks, virtual world laws and regulations, and privacy and data security concerns. We outline future recommendations for enabling a sustainable and user-friendly metaverse ecosystem.
Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit activities, including fraud and money laundering, through anonymous wallets. Identifying wallets involved in large transfers or abnormal transactional patterns is therefore critical to ecosystem security. This study proposes an AI-based framework employing XGBoost, LightGBM, and CatBoost to detect suspicious Ethereum wallets, achieving test accuracies between 95.83% and 96.46%. The system provides near real-time predictions for individual or recent wallet addresses using a pre-trained XGBoost model. To improve interpretability, SHAP (SHapley Additive exPlanations) visualizations are integrated, highlighting the contribution of each feature. The results demonstrate the effectiveness of AI-driven methods in monitoring and securing Ethereum transactions against fraudulent activities.
Purpose This paper reviews the application of game theory in finance, focusing on its role in modeling strategic interactions among market participants. It synthesizes classical models such as Nash equilibrium and signaling games while integrating emerging themes including behavioral finance, sustainability-linked decisions, decentralized finance (DeFi) and artificial intelligence (AI)-driven agents. The study aims to highlight how game-theoretic frameworks inform financial decision-making, market design and governance and to identify conceptual gaps and future research directions. Design/methodology/approach The study employs a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol, complemented by bibliometric mapping using VOSviewer. It analyzes 78 peer-reviewed articles published between 2000 and 2025 across five finance domains: asset pricing, corporate finance, investment strategies, financial markets and behavioral finance. Conceptual frameworks and taxonomies are developed to categorize game-theoretic models by strategic orientation and information structure, offering a structured synthesis of theoretical advancements and practical applications. Findings Game theory enhances understanding of strategic behavior in finance, particularly under conditions of asymmetric information and market complexity. Key findings include the relevance of signaling games in initial public offerings pricing, repeated games in environmental, social and governance commitments and mechanism design in DeFi governance. The review identifies gaps in behavioral integration, empirical validation and modeling of decentralized ecosystems. It proposes future research directions involving multi-agent learning, adaptive mechanism design and sustainability-linked financial strategies. Research limitations/implications The review is limited by its focus on published literature and may exclude emerging models in unpublished or proprietary research. Empirical validation of proposed frameworks remains a future research priority. Practical implications The paper offers actionable insights for regulators, investors and policymakers by applying game-theoretic tools to systemic risk management, portfolio allocation and financial regulation in digitized markets. Originality/value This study provides a novel synthesis of game theory’s evolution in finance, introducing conceptual frameworks that integrate behavioral, technological and sustainability-linked dimensions.
Dynamic trust evaluation in large, rapidly evolving graphs demands models that capture changing relationships, express calibrated confidence, and resist adversarial manipulation. DGTEN (Deep Gaussian-Based Trust Evaluation Network) introduces a unified graph-based framework that does all three by combining uncertainty-aware message passing, expressive temporal modeling, and built-in defenses against trust-targeted attacks. It represents nodes and edges as Gaussian distributions so that both semantic signals and epistemic uncertainty propagate through the graph neural network, enabling risk-aware trust decisions rather than overconfident guesses. To track how trust evolves, it layers hybrid absolute-Gaussian-hourglass positional encoding with Kolmogorov-Arnold network-based unbiased multi-head attention, then applies an ordinary differential equation-based residual learning module to jointly model abrupt shifts and smooth trends. Robust adaptive ensemble coefficient analysis prunes or down-weights suspicious interactions using complementary cosine and Jaccard similarity, curbing reputation laundering, sabotage, and on-off attacks. On two signed Bitcoin trust networks, DGTEN delivers standout gains where it matters most: in single-timeslot prediction on Bitcoin-OTC, it improves MCC by +12.34% over the best dynamic baseline; in the cold-start scenario on Bitcoin-Alpha, it achieves a +25.00% MCC improvement, the largest across all tasks and datasets; while under adversarial on-off attacks, it surpasses the baseline by up to +10.23% MCC. These results endorse the unified DGTEN framework.
The evolution of 5G and emerging 6G networks has introduced unprecedented opportunities for connectivity, but also expanded the attack surface for Distributed Denial of Service (DDoS) amplification attacks. Service-Based Architecture (SBA), network slicing, and massive IoT (mMTC) environments create new vectors for reflection and amplification, making conventional defenses inadequate. This paper proposes a novel layered defense framework that integrates edge filtering, AI-driven anomaly detection, slice isolation, cloud scrubbing, and quantum-safe cryptography to mitigate DDoS amplification attacks in 5G/6G environments. The framework is theoretically modeled through equations for amplification, mitigation efficiency, resilience, and defense cost, and evaluated experimentally using simulated signaling floods, IoT-driven amplification, slice-targeted floods, and hybrid attacks. Performance was measured using detection rate, false alarm rate, service availability, resilience score, and resource overhead. Two algorithms—pseudonymous authentication with zero-knowledge proof (ZKP) and layered mitigation orchestration—were implemented to operationalize the defense strategy. The results demonstrate that the proposed framework achieves a detection accuracy of 95–97%, reduces false positives to 2%, and maintains a service availability of over 85% under prolonged amplification attacks. It scales efficiently in scenarios with up to 10,000 simulated IoT devices, retaining 70–80% throughput, and maintains URLLC latency below 10 ms, outperforming baseline defenses (firewalls, scrubbing, and AI-only) and state-of-the-art defenses from the literature. These findings validate the framework as a scalable, efficient, and future-ready solution for mitigating amplification attacks in 5G/6G networks, with strong alignment with 3GPP, GSMA, and NIST post-quantum standards.
Open access
Advanced Malware Detection Techniques
Network Security and Intrusion Detection
Physical Unclonable Functions (PUFs) and Hardware Security
Марат Рашитович Сафиуллин, Leonid Alekseevich Elshin, Yaroslav Kuznetsov
Objective: This study seeks to substantiate the prospects for using blockchain technologies as a mechanism to attract Islamic finance to the Russian regions, with the dual aim of mitigating sanctions-related restrictions and fostering integration into global Islamic financial ecosystems. Methodology/Approach: The research employs econometric and systems analysis to assess the macroeconomic externalities of blockchain-driven Islamic finance inflows. A methodological toolkit was developed and tested to estimate potential market capacity, using data from four Russian regions (Tatarstan, Bashkortostan, Chechnya, Dagestan) through 2030. The approach incorporates substitution modeling of lost Western capital, scenario analysis, and the application of blockchain-based financial gateways. Originality/Relevance: The originality of this work lies in linking two underexplored areas—Islamic finance and blockchain technologies—in the context of Russia’s geoeconomic reorientation toward Asia and the Global South. The study provides an innovative framework for replacing Western capital flows with investments from Islamic finance markets through decentralized fintech solutions. Main Conclusion: Findings demonstrate that the use of blockchain-based financial mechanisms can significantly expand the capacity of Russian regions to attract Islamic finance. Tatarstan and Bashkortostan show the highest potential, while Chechnya and Dagestan present smaller but strategically relevant capacities. Blockchain solutions are positioned as a breakthrough tool for overcoming international financial isolation and enabling long-term convergence with Islamic digital ecosystems. Theoretical/Methodological Contribution: The study advances the methodological basis for assessing fintech’s role in regional investment attraction by introducing a quantitative model that integrates substitution coefficients, market capitalization ratios, and penetration indices. It enriches the theoretical discourse on blockchain’s economic externalities and provides policymakers and practitioners with actionable instruments for embedding Islamic finance within regional development strategies.
Cryptocurrency – a digital currency based on decentralized blockchain technology has been controversial since the creation of the first cryptocurrency, Bitcoin in 2009. While some people believe that it brings a revolutionary technological tool which has the potential to challenge the existing financial architecture or even destroy it all together, others consider it as a classic bubble created by hyped expectations. This review aims at explaining the technique that forms the basis of cryptocurrencies, how they have been implemented in different industries, and the advantages and disadvantages of the technology. Topics include financial accessibility, transaction velocity, and emergence of decentralised finance, notable issues involve unpredictability, the lack of clarity concerning regulation, and concerns about the effects on the environment. Lastly, the paper concludes whether introducing the cryptocurrency is revolutionary in the field of finance or just another fad.
Bitcoin transaction anomaly detection is essential for maintaining financial market stability. A significant challenge is capturing the dynamically evolving transaction patterns within transaction networks. Dynamic graph models are effective for characterizing the temporal evolution of transaction systems. However, current methods struggle to mine long-range temporal dependencies and address the class imbalance caused by the scarcity of abnormal samples. To address these issues, we propose a novel approach, the Bidirectional EvolveGCN with Class-Balanced Learning Network (Balanced-BiEGCN), for Bitcoin transaction anomaly detection. This model integrates two key components: (1) a bidirectional temporal feature fusion mechanism (Bi-EvolveGCN) that enhances the capture of long-range temporal dependencies and (2) a Sample Class Transformation (CSCT) classifier that generates difficult-to-distinguish abnormal samples to balance the positive and negative class distribution. The generation of these samples is guided by two loss functions: the adjacency distance adaptive loss function and the symmetric space adjustment loss function, which optimize the spatial distribution and confusion of abnormal samples. Experimental results on the Elliptic dataset demonstrate that Balanced-BiEGCN outperforms existing baseline methods in anomaly detection.
Smart contracts—auto-executing digital agreements built on DLT (Distributed Ledger Technology), an emerging technology of blockchain—are revolutionizing cross-border payments by enhancing efficiency and automation. However, their widespread adoption is hindered by a fragmented regulatory landscape and legal uncertainties across jurisdictions. Therefore, to promote the urgency of regulatory governance of smart contract, this research advocates for the techno-legal standardization of smart contracts to ensure regulatory compliance in international financial transactions. It investigates how smart contracts can be designed to meet diverse legal requirements while maintaining technical adaptability, scalability, and interoperability. Drawing on interdisciplinary literature and qualitative methods—including expert interviews, surveys, and case studies—the study aims to develop a framework that balances innovation with legal certainty. Key challenges addressed include jurisdictional fragmentation, enforcement mechanisms, integration with legacy systems like SWIFT, and compliance with KYC/AML regulations. The research also examines emerging solutions such as decentralized identity frameworks, trusted oracles, and hybrid on-chain/off-chain models. By bridging the gap between law, technology, and finance, this study offers actionable insights for policymakers, financial institutions, blockchain developers, and international businesses. Ultimately, it contributes to the development of a standardized smart contract ecosystem that supports secure, efficient, and legally compliant cross-border payments.
Rabbiya Younas, Hafiz Muhammad Raza Ur Rehman, Gyu Sang Choi
Cryptocurrencies function as a digital exchange medium operating on network-based technology, where records are secured using cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). These cryptocurrencies utilize blockchain technology to provide transparent, reliable, and immutable transactions. Consequently, cryptocurrencies have gained significant traction across multiple sectors, particularly finance. However, their value is still prone to considerable fluctuations, which raises concerns about the risks associated with investments. The emerging discipline of cryptocurrency forecasting has gained popularity worldwide, and academics are employing a variety of deep learning (DL) and machine learning (ML) techniques to investigate the elements that influence cryptocurrency values. Among the various DL methods, LSTM has demonstrated noteworthy efficiency. Nevertheless, there are intrinsic downsides to LSTM, notably due to its sequential nature, which hinders parallelization and complicates the modeling of both short- and long-term dependencies. To address these shortcomings, the Transformer architecture has emerged as a potent solution. The Transformer is widely used in DL for its exceptional parallelization capabilities and its capacity to extract broad, distant data dependencies. Recent studies have explored Transformer-based approaches for cryptocurrency price forecasting, particularly for modeling long-term dependencies. However, these models often exhibit limitations in capturing high-frequency, short-term fluctuations, making them less suitable for short-term prediction tasks. Our proposed methodology introduces a novel Transformer-based hybrid framework designed to enhance forecasting accuracy across various time scales. We evaluate the forecasting accuracy for 10 cryptocurrencies at hourly, daily, and yearly frequencies. The findings show that, in comparison to other DL techniques such as LSTM, RNN, and baseline Autoformer, our model achieves superior accuracy. Furthermore, we benchmark our method against prominent Transformer variants such as Informer and FEDformer, and observe improved performance in both short- and long-term forecasting scenarios. These results indicate that our proposed model consistently outperforms existing state-of-the-art Transformer-based approaches in cryptocurrency price prediction.