Non-fungible tokens (NFTs) have gained mainstream attention in the fintech community, but there is little research on their statistical properties. This study investigates the long-memory characteristics of NFT returns and volatility, focusing on their potential for predicting price movements. As NFTs do not conform to traditional models, understanding their unique features is crucial for comprehending complex market dynamics. This study aims to reveal the impact of macroeconomic factors on NFT prices, understand their correlation and develop predictive models using autoregression and artificial intelligence (AI) technology. This research utilized datasets from the Centers for Disease Control and Prevention (CDC), U.S. Bureau of Labor Statistics, Bureau of Economic Analysis, Christie’s, Dune, and Google Trends. Correlation and p value tests revealed strong relationships between NFT prices and variables such as weekly volume, pandemics, inflation and security. The Baseline Model using autoregression with NFT volume, security and technology factors outperformed all other models demonstrating the speculative volatility of NFTs. The Transformer Model using transformers, an architecture used by ChatGPT, Gemini and Stable Diffusion, showed high accuracy with less feature selection and preprocessing efforts. This study provides a novelty using a systematic approach for researchers to perform financial forecasting and contributes to the scarce literature on NFTs. This research offers valuable insights to investors and private agents regarding the right economic conditions for NFT investments by reducing portfolio risks and making informed decisions. To the authors’ best knowledge, this is the first study to utilize time-series transformers for forecasting NFTs based on macroeconomic factors.
Jaime Torres, Sergio A. Ortega, Miguel A. Martin-Delgado
The Quantum Signature Validation Algorithm (QSVA) is introduced as a novel quantum-based approach designed to enhance the detection of tampered transactions in blockchain systems. Leveraging the powerful capabilities of quantum computing, especially within the framework of transaction-based blockchains, the QSVA aims to surpass classical methods in both speed and efficiency. By utilizing a quantum walk approach integrated with PageRank-based search algorithms, QSVA provides a robust mechanism for identifying fraudulent transactions. Our adaptation of the transaction graph representation efficiently verifies transactions by maintaining a current set of unspent transaction outputs (UTXOs) characteristic of models like Bitcoin. The QSVA not only amplifies detection efficacy through a quadratic speedup but also incorporates two competing quantum search algorithms$-$Quantum SearchRank and Randomized SearchRank$-$to explore their effectiveness as foundational components. Our results indicate that Randomized SearchRank, in particular, outperforms its counterpart in aligning with transaction rankings based on the Classical PageRank algorithm, ensuring more consistent detection probabilities. These findings highlight the potential for quantum algorithms to revolutionize blockchain security by improving detection times to $O(\sqrt{N})$. Progress in Distributed Ledger Technologies (DLTs) could facilitate future integration of quantum solutions into more general distributed systems. As quantum technology continues to evolve, the QSVA stands as a promising strategy offering significant advancements in blockchain efficiency and security.
One of the goals of Federated Learning (FL) is to collaboratively train a global model using local models from remote participants. However, the FL process is susceptible to various security challenges, including interception and tampering models, information leakage through shared gradients, and privacy breaches that expose participant identities or data, particularly in sensitive domains such as medical environments. Furthermore, the advent of quantum computing poses a critical threat to existing cryptographic protocols through the Shor and Grover algorithms, causing security concerns in the communication of FL systems. To address these challenges, we propose a Post-Quantum Blockchain-based protocol for Federated Learning (PQBFL) that utilizes post-quantum cryptographic (PQC) algorithms and blockchain to enhance model security and participant identity privacy in FL systems. It employs a hybrid communication strategy that combines off-chain and on-chain channels to optimize cost efficiency, improve security, and preserve participant privacy while ensuring accountability for reputation-based authentication in FL systems. The PQBFL specifically addresses the security requirement for the iterative nature of FL, which is a less notable point in the literature. Hence, it leverages ratcheting mechanisms to provide forward secrecy and post-compromise security during all the rounds of the learning process. In conclusion, PQBFL provides a secure and resilient solution for federated learning that is well-suited to the quantum computing era.
In a rapidly evolving landscape marked by continuous change and complex challenges, effective cash management stands as a cornerstone for ensuring business sustainability and driving performance. To address these pressing demands, cash managersare increasingly turning to innovative financing solutions such as venture capital, green finance, crowdfunding, advanced services from Pan-African banks, and blockchain technology. These cutting-edge tools are pivotal in bolstering resilience against market volatility, ecological transitions, and the accelerating pace of technological change. The present article aims to examine how such innovative financial approaches can serve as strategic drivers, enabling businesses to transform challenges into opportunities. The analysis underscores that rethinking cash management through innovation is a critical pathway toboost the performance of Moroccan companies. Therefore, embracing these forward-thinking strategies unlocks new avenues for development empowering them to adapt with agility amidst the uncertainties of a shifting environment.
Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems often rely on centralized aggregating mechanisms, introducing trust issues, single points of failure, and limited mechanisms for incentivizing meaningful client contributions. These challenges are exacerbated as FL scales to train resource-intensive models, such as large language models (LLMs), requiring scalable, decentralized solutions. This paper presents a blockchain-based FL framework that addresses these limitations by integrating smart contracts and a novel hybrid incentive mechanism. The framework automates critical FL tasks, including client registration, update validation, reward distribution, and maintaining a transparent global state. The hybrid incentive mechanism combines on-chain alignment-based rewards, off-chain fairness checks, and consistency multipliers to ensure fairness, transparency, and sustained engagement. We evaluate the framework through gas cost analysis, demonstrating its feasibility for different scales of federated learning scenarios.
Mobile ad hoc networks (MANETs) facilitate data communication across multiple nodes and hop stations, characterized by their dynamic topology. This inherent flexibility, however, makes MANETs vulnerable to various security threats, notably blackhole and wormhole attacks, where malicious nodes can intercept and manipulate data. This study investigates the security vulnerabilities of MANETs, particularly against blackhole, Sybil, and wormhole attacks, and introduces the Advanced Blockchain Dynamic Source Routing (ABCD) algorithm to address these challenges. Motivated by the need for robust and decentralized security solutions in MANETs, the proposed algorithm integrates blockchain technology and homomorphic encryption to secure data communication without intermediate decryption. The ABCD algorithm leverages Dijkstra’s algorithm for optimal routing and employs a tamper-proof, decentralized data storage approach. Comparative analysis under attack scenarios reveals that the ABCD algorithm outperforms the standard DSR protocol across multiple quality of service metrics, demonstrating a significant improvement in MANET security over equivalent studies. The packet delivery rate is also improved from 81 to 92% using the modified ABCD algorithm.
Data security during transmission over public networks has become a key concern in an era of rapid digitization. Image data is especially vulnerable since it can be stored or transferred using public cloud services, making it open to illegal access, breaches, and eavesdropping. This work suggests a novel way to integrate blockchain technology with a Chaotic Tent map encryption scheme in order to overcome these issues. The outcome is a Blockchain driven Chaotic Tent Map Encryption Scheme (BCTMES) for secure picture transactions. The idea behind this strategy is to ensure an extra degree of security by fusing the distributed and immutable properties of blockchain technology with the intricate encryption offered by chaotic maps. To ensure that the image is transformed into a cipher form that is resistant to several types of attacks, the proposed BCTMES first encrypts it using the Chaotic Tent map encryption technique. The accompanying signed document is safely kept on the blockchain, and this encrypted image is subsequently uploaded to the cloud. The integrity and authenticity of the image are confirmed upon retrieval by utilizing blockchain's consensus mechanism, adding another layer of security against manipulation. Comprehensive performance evaluations show that BCTMES provides notable enhancements in important security parameters, such as entropy, correlation coefficient, key sensitivity, peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), and number of pixels change rate (NPCR). In addition to providing good defense against brute-force attacks, the high key size of [Formula: see text] further strengthens the system's resilience. To sum up, the BCTMES effectively addresses a number of prevalent risks to picture security and offers a complete solution that may be implemented in cloud-based settings where data integrity and privacy are crucial. This work suggests a promising path for further investigation and practical uses in secure image transmission.
Open access
Chaos-based Image/Signal Encryption
Advanced Steganography and Watermarking Techniques
The rapid development of digital currency is driving profound changes in the global financial system. This paper analyzes the impact of digital currency on the traditional financial system across several areas, including payment and settlement, monetary policy, financial intermediation, and the international financial landscape. Firstly, digital currency enhances the efficiency of payment and settlement through decentralized technology, challenging the dominant role of traditional banks within the payment systems. Secondly, the introduction of central bank digital currencies (CBDCs) has far-reaching implications for monetary policy tools and may strengthen central banks’ control over the economy. Meanwhile, the rise of decentralized finance (DeFi) weakens the intermediation role of traditional financial institutions, transforming patterns of capital flow. At an international level, the widespread adoption of digital currency may reshape the global financial order, reduce the influence of reserve currencies such as the U.S. dollar, and promote the liberalization of capital flows.
Stablecoins serve as the backbone of many decentralized finance (DeFi) ecosystems, offering price stability in an otherwise volatile cryptocurrency market. This paper analyzes the economic design of stablecoins- both algorithmic (un- or under-collateralized) and asset-backed (collateralized)- and employs game-theoretic models to examine their susceptibility to speculative attacks. We present mathematical frameworks illustrating peg- maintenance mechanisms, discuss equilibrium conditions for stable pegging, and use real-world examples of USDC, DAI, and Terra-Luna to highlight the key success and failure factors. Policy and protocol design recommendations are provided to help mitigate risks of de-pegging and bank-run dynamics.
This study investigates the underlying motivations of lenders in Peer-to-Peer (P2P) lending, using the Theory of Planned Behavior as its framework. Based on qualitative interviews, findings reveal that lenders exhibit a positive attitude toward P2P lending, driven by opportunistic investment decisions, the perception of relatively low-risk investments, and the availability of disposable income. This attitude is further reinforced by the accessibility of investment-related information on digital platforms. Beyond individual motivations, social influence plays a crucial role, as family members and digital influencers significantly impact lenders' investment choices. Additionally, perceived behavioral control in digital investment environments is shaped not only by regulatory structures and platform transparency but also by decentralized information sources, investment flexibility, and experiential learning. These findings emphasize the transformative role of digital finance in shaping investor autonomy and risk perception. The study offers practical insights for P2P lending platforms to develop more effective communication and engagement strategies tailored to digitally savvy investors.
Owing to the swift advancement of technology and the unfamiliarity of the execution environment, the development of Solidity smart contracts from scratch often results in significant vulnerabilities.In contrast, automated code generation enhances productivity, minimizes development time, and enables developers to focus on high-level tasks and fundamental logic.In consideration of these two viewpoints, this paper examines the utilization of large language models (LLMs) for the automatic generation of Solidity smart contracts based on specified criteria, while simultaneously ensuring the elimination of vulnerabilities through a novel masking strategy.To achieve this, we propose SolGen, a framework for generating secure Solidity smart contract code using LLMs.We assess the performance of existing LLMs (i.e.ChatGPT and Meta AI) for secure Solidity code generation.Our research indicates that ChatGPT outperforms Meta AI in performance, yielding a greater percentage of syntactically accurate and secure code.Additionally, we examine the impact of temperature adjustment on the security of generated contracts using an open-source LLM, Llama3.Our findings suggest that a temperature setting of 0.7 is optimal for the generation of Solidity code, considerably exceeding the performance of both lower and higher settings (0.1 and 1.2), especially with regard to the compilability of the code.
Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han · 8 authors
Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PartitionGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into privileged and normal codebases, guided by a few annotated sensitive data variables. We evaluated PartitionGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PartitionGPT successfully generates compilable, and verified partitions for 78% of the sensitive functions while reducing approximately 30% code compared to function-level partitioning approach. Furthermore, we evaluated PartitionGPT on nine real-world manipulation attacks that lead to a total loss of 25 million dollars, PartitionGPT effectively prevents eight cases, highlighting its potential for broad applicability and the necessity for secure program partitioning during smart contract development to diminish manipulation vulnerabilities.
The popularity of smart contracts has cemented their place in the Blockchain Ecosystem.This is because of the immense number of use cases smart contracts provide.They have become the go-to solution for improving transparency and security for all parties involved in the transaction.Furthermore, a smart contract is immutable after it is deployed.Thus optimization of the smart contract is very important before deployment.Sol-Repairer is a tool that provides the implementation for identifying dead code segments from solidity-written smart contracts and then repairing them.Extensive experiments show that Sol-Repairer optimizes dead code better than the solidity compiler.The study also demonstrates that optimizing dead code reduces gas consumption significantly for smart contracts. CCS Concepts• Software and its engineering → Software testing and debugging.
Phuc-Hung Pham Le, Trung-Tin Tran, Toan Q. Dinh, Quy N.
As end-to-end encryption (E2EE) becomes the standard for secure communication, ensuring message authenticity while maintaining user privacy poses significant challenges.This paper introduces the BL0K-ME protocol, a novel cryptographic solution that combines Zero-Knowledge Proofs (ZKP), RSA encryption, and Bloom filters to authenticate individual messages within E2EE conversations.RSA encryption is employed to secure the transmission of messages between users, ensuring that only the intended recipient can decrypt the content, while ZKP enables third-party verification of specific message content without exposing the entire conversation.By leveraging Bloom filters, the protocol provides efficient logging and verification of message existence, balancing privacy protection with legal and regulatory requirements for digital evidence.BL0K-ME addresses a critical gap in current messaging systems by allowing service providers to verify message authenticity for legal investigations without compromising the confidentiality of unrelated communications.This research demonstrates the potential of integrating RSA encryption, ZKP, and Bloom filters to offer a scalable, secure solution for message authentication in E2EE systems, safeguarding both user privacy and the integrity of digital evidence.
This paper presents method for transforming education funding through a blockchain-powered crowdfunding platform. This platform aims to empower students, educators, and educational institutions. Our goal is to create a decentralized environment where creative educational projects can receive financial support, providing assistance to students facing financial barriers and enabling small schools to request funding for infrastructure and program improvements. In response to the increasing demand for transparency and accountability in crowdfunding, our platform encourages collaboration among developers to enhance features and functionalities, all while offering a user-friendly interface through Web3 technology. The central focus is on fostering community involvement and attracting backing for educational initiatives, while also allowing the platform to evolve and scale efficiently with the benefits of Polygon's scalability
Decentralized Autonomous Organizations (DAOs) have emerged as a revolutionary alternative to traditional governance structures, offering transparency, efficiency, and community-driven decision-making. This paper explores the core characteristics of DAOs, their potential applications in social governance, and their challenges compared to traditional government institutions. Through case studies, including CityDAO, Gitcoin Grants, UkraineDAO, VitaDAO, Proof of Humanity, and Kleros, we analyze real-world implementations of DAO governance. Despite the advantages, DAOs face legal uncertainties, governance inefficiencies, and security vulnerabilities that hinder their broader adoption. The study further examines the prospects of integrating DAOs into traditional governance frameworks and the future evolution of decentralized governance models. Addressing these challenges through technological innovation and regulatory adaptation will be crucial for DAOs to play a sustainable role in global governance.
We live in an era of unprecedented transformation, driven by digital platforms that are redefining the creation, distribution, and consumption of creative content. The emergence of new digital platforms such as YouTube, TikTok, and Patreon has led to new forms of social inequality in the economy, AI ethics, and environmental sustainability. Artificial Intelligence (AI) and Augmented Reality (AR) enhance content personalization and production efficiency, yet algorithmic opacity and revenue concentration create disparities that favor established creators. This study investigates the impact of digital platforms on creative industries through a qualitative methodology, integrating secondary data from global reports (e.g., UNCTAD, 2022) and case studies of TikTok, Patreon, and emerging Web3 alternatives. Findings highlight both opportunities—such as decentralized revenue models, AI-driven innovation, and regional creative growth—and challenges, including the monopolization of content distribution, ethical dilemmas in AI-generated creativity, and the environmental burden of data centers and blockchain transactions. This study focuses on the impact of Web3 technologies on the reliance of intermediaries while covering contemporary regulations on AI governance and platform responsibility. The study highlights the gap in policy efforts that ensure economic equity, transparency, and platform sustainability that need to be addressed. Suggested policy targets include more humane monetization of smaller creator's content, more responsible AI regulation, and the creation of less environmentally harmful digital systems. This study is situated within the larger discussion of the intersection of innovation, equity, and sustainability in the context of a developing digital creative economy.
Open access
Cultural Industries and Urban Development
University-Industry-Government Innovation Models
Innovative Approaches in Technology and Social Development
Abstract Applying Distributed Ledger Technologies to securely manage intercommunicated data between IoT applications has recently been adopted on an enormous scale. They enable data integrity, privacy, and robustness to public, open, permission-less P2P networks. Voting-based consensus algorithms proved high efficiency even with limited computing and less power IoT devices. Moreover, they can identify legitimate information and isolate malicious attackers through repetitive voting queries to adjacent peers asking their opinions about the validity of each transaction. Several lightweight validation models are introduced to enrich IoT networks with better performance and higher security. Nevertheless, the current algorithms struggle to find adequate parameters that balance network security and operability, in addition to balancing fairness in distributed environments. This paper introduces an Autonomous Lightweight Ledger Constructor to resolve common defects and threats. Based on Reinforcement Learning, it can dynamically construct a valid distributed ledger in limited-computing systems under several adversarial conditions. The validity of transactions in this approach is calculated based on their cumulative weights and the issuer’s reputation, which are inferred subjectively by a lightweight Bayesian-like function. A new simulator is developed to evaluate ALLC performance and security. The experimental results demonstrate reasonable performance and high resistance against known compromises targeting Distributed Ledger Technologies.
Mr. R. Suresh M. E, Mr. Mohamed Shalik. S, Mr. Joshva Jagan. A, Mr. Vigneshwaran. V
This venture presents a secure and private Web3 communication framework utilizing the Dual Reversible Secret Image Sharing Mechanism (DR-SISM). By utilizing wallet addresses for confirmation, it streamlines the method whereas guaranteeing security. DR-SISM safely encodes images into numerous offers, available as it were by the expecting beneficiary. The framework moreover coordinating AI-driven command help, permitting clients to send messages and share images utilizing normal dialect commands, making the stage user-friendly. This paper looks at existing communication advances and illustrates how combining DR-SISM, wallet-based informing, and AI makes a secure, adaptable Web3 arrangement.
Formal verification plays a crucial role in making smart contracts safer, being able to find bugs or to guarantee their absence, as well as checking whether the business logic is correctly implemented. For Solidity, even though there already exist several mature verification tools, the semantical quirks of the language can make verification quite hard in practice. Move, on the other hand, has been designed with security and verification in mind, and it has been accompanied since its early stages by a formal verification tool, the Move Prover. In this paper, we investigate through a comparative analysis: 1) how the different designs of the two contract languages impact verification, and 2) what is the state-of-the-art of verification tools for the two languages, and how do they compare on three paradigmatic use cases. Our investigation is supported by an open dataset of verification tasks performed in Certora and in the Aptos Move Prover.
The convergence of Operational Technology (OT) and Information Technology (IT) under Industry~4.0 has widened the cyber-attack surface of critical infrastructure across manufacturing, energy, transportation, water, and healthcare. This survey synthesizes OT/IT cybersecurity along four axes. First, we taxonomize \emph{attack vectors} that traverse the IT--OT boundary, separating IT-side initial access (phishing, exploits, supply-chain compromise, exposed remote access) from OT-side propagation and impact (insecure protocols, weak authentication, firmware tampering, control-logic manipulation). Second, we review \emph{defensive technologies} -- signature-based intrusion detection, AI/ML anomaly detection, Zero Trust Architecture, blockchain-based event logging, digital twins, and OT-aware Security Operations Centers -- and identify remaining \emph{gaps}: OT-specific patch management, dataset scarcity for ML, IoMT segmentation, and the absence of consistent resilience metrics. Third, we compile a cross-validated \emph{historical record} of 69 high-impact incidents spanning 2010--2025, from Stuxnet to Jaguar Land Rover, and quantify their \emph{commercial effects} sector by sector using figures sourced from SEC filings, government post-incident reviews, and primary regulatory disclosures. Fourth, we map the \emph{regulatory landscape}: NIST Cybersecurity Framework2.0 and SP~800-82~Rev.~3, IEC~62443, the EU NIS2 Directive, DORA, the Cyber Resilience Act, NERC~CIP, and healthcare-specific regimes (IEC-80001-1, FDA, NIST-SP-1800-8). A sectoral deep-dive on healthcare illustrates the IT--OT convergence threat model under high-consequence conditions. The result is a single, source-traceable reference on where OT/IT cybersecurity stands, what the historical record costs defenders who lag, and where investment yields the highest marginal return on resilience.
Volume-Weighted Average Price (VWAP) is arguably the most prevalent benchmark for trade execution as it provides an unbiased standard for comparing performance across market participants. However, achieving VWAP is inherently challenging due to its dependence on two dynamic factors, volumes and prices. Traditional approaches typically focus on forecasting the market's volume curve, an assumption that may hold true under steady conditions but becomes suboptimal in more volatile environments or markets such as cryptocurrency where prediction error margins are higher. In this study, I propose a deep learning framework that directly optimizes the VWAP execution objective by bypassing the intermediate step of volume curve prediction. Leveraging automatic differentiation and custom loss functions, my method calibrates order allocation to minimize VWAP slippage, thereby fully addressing the complexities of the execution problem. My results demonstrate that this direct optimization approach consistently achieves lower VWAP slippage compared to conventional methods, even when utilizing a naive linear model presented in arXiv:2410.21448. They validate the observation that strategies optimized for VWAP performance tend to diverge from accurate volume curve predictions and thus underscore the advantage of directly modeling the execution objective. This research contributes a more efficient and robust framework for VWAP execution in volatile markets, illustrating the potential of deep learning in complex financial systems where direct objective optimization is crucial. Although my empirical analysis focuses on cryptocurrency markets, the underlying principles of the framework are readily applicable to other asset classes such as equities.
Yixuan Liu, Yuxin Dong, Ye Liu, Xiapu Luo · 5 authors
With the rapid development of blockchain technology, transaction logs play a central role in various applications, including decentralized exchanges, wallets, cross-chain bridges, and other third-party services. However, these logs, particularly those based on smart contract events, are highly susceptible to manipulation and forgery, creating substantial security risks across the ecosystem. To address this issue, we present the first in-depth security analysis of transaction log forgery in EVM-based blockchains, a phenomenon we term Phantom Events. We systematically model five types of attacks and propose a tool designed to detect event forgery vulnerabilities in smart contracts. Our evaluation demonstrates that our approach outperforms existing tools in identifying potential phantom events. Furthermore, we have successfully identified real-world instances for all five types of attacks across multiple decentralized applications. Finally, we call on community developers to take proactive steps to address these critical security vulnerabilities.
Cryptocurrency exchange in Asian emerging countries has grown rapidly in the past few years while there’s still inconsistency in the supervision of the official agency such as Central Bank and the Securities and Exchange Commission which made it unclear about the future direction of cryptocurrency exchange. This paper analyses herding behavior in 4 cryptocurrency exchanges which located in 4 emerging countries in Asian, using total of 18,313,994 trading data both daily and 4 types of high frequency data (240-min, 60-min, 30-min, and 15-min) for the period from 1st January 2019 to 30th November 2024 by the Cross-sectional absolute deviations (CSAD) approach. The result suggests that herding behavior always evident when apply the daily data but undetected with high-frequency data which is more appropriate data. This finding cast light on the efficiency of cryptocurrency exchange in Asian emerging countries and leads to a guideline for official authorities to regulate the market appropriately.