The article is devoted to the analysis of the problem of the development of the cryptocurrency market, the markets of their circulation and the prospect of using them as a means of payment in international settlements of the Russian Federation in the context of increasing sanctions pressure. The author analyzes a group of social and economic factors that led to the emergence of cryptocurrencies. Special attention is paid to the disclosure of their technological essence of cryptocurrencies and their rewarding nature. The article examines in detail the history of the development of cryptocurrencies themselves, their circulation markets, the business essence of this means of payment and the path of its entry into the global economic system. The paper compares the European, Russian and Chinese ways of regulating the cryptocurrency market, the consequences, causes and features of the use of various methods of regulating the crypto market for national economies and their impact on the cost and demand of cryptocurrencies. Special attention is paid to the study of the problem of the practical use of cryptocurrencies on the territory of the Russian Federation and in its international settlements both at the state level and on the scale of individual enterprises. The author evaluates the possible prospects for the development of the Russian national cryptocurrency, and the use of bitcoin as an alternative means of domestic and international settlements of the Russian Federation. It is this aspect of the development of cryptocurrencies that is particularly important for the Russian market, which is in dire need of a means of payment that will help circumvent Western sanctions. The paper also reflects the problem of the attractiveness of the cryptocurrency market for criminal transactions and the difficulty of countering illegal transactions without seriously compromising the attractiveness of cryptocurrencies as a means of payment.
Decentralized Finance (DeFi) faces critical security challenges due to its pseudonymous and permissionless nature, which exposes it to fraud and market instability. Existing approaches, such as single-agent reinforcement learning (RL) and static graph-based fraud detection, struggle to capture dynamic multi-agent interactions and evolving financial risks. This study proposes an integrated framework combining Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNNs) to address adaptive decision-making and real-time fraud detection in DeFi. MARL agents, trained in DeepMind’s Melting Pot environment, optimize trading, liquidity provisioning, and arbitrage strategies, while GNNs analyze transaction graphs to detect anomalous patterns. Experimental results demonstrate that MARL agents achieve a 210% increase in average profit per trade and a 57% improvement in market adaptation, alongside a 120% rise in liquidity utilization. The GNN model attains a converged loss below 0.10, reducing false positives by 29%. The integrated system enhances market stability, achieving a stability impact score of 175 within 10 training episodes. This work establishes a scalable, intelligent framework for fraud-resistant trading, cross-chain compliance, and decentralized risk management, advancing the security and efficiency of DeFi ecosystems.
In the field of cyber security, while blockchain technology is renowned for its robust security, the blockchain smart contracts suffer of various vulnerabilities that attackers can easily exploit to launch attacks, resulting in irreversible losses. Therefore, ensuring the security of smart contracts is crucial given the widespread adoption of this technology in our society. At present, although there are many traditional methods used for vulnerability detection, these methods generally have certain limitations. With the rapid development of deep learning, AI (particularly generative AI) has gradually become mainstream in the field of software engineering vulnerability detection, offering new opportunities for enhancing smart contract security. In this paper, we investigate how advanced large language models (LLMs) can be used to tackle vulnerability detection, not only to identify vulnerability but also to provide remediation suggestions for fixing vulnerable smart contracts. We introduce, through a new guideline-based Framework, a suitable application process for using LLMs in smart contract vulnerability detection activity using less computing resource.
Fausto Neri da Silva Vanin, Rodrigo da Rosa Righi, Cristiano André da Costa
Blockchain technology in healthcare is gaining attention for addressing data privacy, interoperability, and health record integrity issues. Standards like HL7 FHIR and OpenEHR ensure data consistency, but privacy concerns persist under regulations like HIPAA, GDPR, and LGPD. Existing methods often store only data hashes, raising validation risks. The MEPCA model introduces a blockchain-based framework for secure health record management, focusing on on-chain EHR data processing. Key elements include Data Steward, Shared Data Vault, and Zero-Knowledge Proofs of HL7 FHIR fields. Experiments with Fully Homomorphic Encryption show enhanced security and reliability for health records, offering a robust alternative to traditional off-chain approaches.
Vincenzo P. Di Perna, Marco Bernardo, Francesco Fabris, Sebastião Amaro · 6 authors
Since blockchains are increasingly adopted in real-world applications, it is of paramount importance to evaluate their performance across diverse scenarios.Although the network infrastructure plays a fundamental role, its impact on performance remains largely unexplored.Some studies evaluate blockchain in cloud environments, but this approach is costly and difficult to reproduce.We propose a cost-effective and reproducible environment that supports both cluster-based setups and emulation capabilities and allows the underlying network topology to be easily modified.We evaluate five industry-grade blockchains -Algorand, Diem, Ethereum, Quorum, and Solana -across five network topologies -fat-tree, full mesh, hypercube, scale-free, and torus -and different realistic workloads -smart contract requests and transfer transactions.Our benchmark framework, Lilith, shows that full mesh, hypercube, and torus topologies improve blockchain performance under heavy workloads.Algorand and Diem perform consistently across the considered topologies, while Ethereum remains robust but slower.
Alternative financial services (AFS) have been studied in recent years in terms of how these financialmarkets are utilized. The products and services include check cashing, pawnshop loans, payday advanceloans, electronic cash transmissions, tax refund anticipation arrangements, rent-to-own contracts, prepaiddebit cards, gift cards, and loans collateralized by automobile titles. Cryptocurrency has become part ofthis AFS ecology. The 2023 Survey of Household Economics and Decisionmaking collected informationon AFS use, including the use of cryptocurrency as an AFS. This research answered the questions: a) Dousers of cryptocurrencies for AFS also tend to use them for investments; b) do users of cryptocurrencies tomake payments tend to use them for other AFS purposes, and c) do users of cryptocurrencies to sendmoney to friends and family tend to use them for other AFS purposes?
Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA.
Smart contract (SC) fuzzing is a critical technique for detecting vulnerabilities in blockchain applications. However, its adoption remains challenging for practitioners due to fundamental differences between SCs and traditional software systems. In this study, we investigate the challenges practitioners face when adopting SC fuzzing tools by conducting an inductive content analysis of 381 GitHub issues from two widely used SC fuzzers: Echidna and Foundry. Furthermore, we conducted a user study to examine how these challenges affect different practitioner groups, SC developers, and traditional software security professionals, and identify strategies practitioners use to overcome them. We systematically categorize these challenges into a taxonomy based on their nature and occurrence within the SC fuzzing workflow. Our findings reveal domain-specific ease-of-use and usefulness challenges, including technical issues with blockchain emulation, and human issues with a lack of accessible documentation and process automation. Our results provide actionable insights for tool developers and researchers, guiding future improvements in SC fuzzer tool design.
The explosive growth of Non-Fungible Tokens (NFTs) has revolutionized digital ownership by enabling the creation, exchange, and monetization of unique assets on blockchain networks. However, this surge in popularity has also given rise to a disturbing trend: the emergence of rug pulls - fraudulent schemes where developers exploit trust and smart contract privileges to drain user funds or invalidate asset ownership. Central to many of these scams are hidden backdoors embedded within NFT smart contracts. Unlike unintentional bugs, these backdoors are deliberately coded and often obfuscated to bypass traditional audits and exploit investor confidence. In this paper, we present a large-scale static analysis of 49,940 verified NFT smart contracts using Slither, a static analysis framework, to uncover latent vulnerabilities commonly linked to rug pulls. We introduce a custom risk scoring model that classifies contracts into high, medium, or low risk tiers based on the presence and severity of rug pull indicators. Our dataset was derived from verified contracts on the Ethereum mainnet, and we generate multiple visualizations to highlight red flag clusters, issue prevalence, and co-occurrence of critical vulnerabilities. While we do not perform live exploits, our results reveal how malicious patterns often missed by simple reviews can be surfaced through static analysis at scale. We conclude by offering mitigation strategies for developers, marketplaces, and auditors to enhance smart contract security. By exposing how hidden backdoors manifest in real-world smart contracts, this work contributes a practical foundation for detecting and mitigating NFT rug pulls through scalable automated analysis.
The emergence of Non-Fungible Tokens (NFTs) – unique, blockchain-based tokens – has introduced a new dimension to the concept of property rights in the digital domain. Recent legal developments in the UK and the proposal of the Property (Digital Assets etc) Bill fuelled the discussion on how to legally conceptualise digital assets, including Non-Fungible Tokens (NFTs). This paper explores the evolving legal landscape surrounding property rights over NFTs, examining the challenges and ambiguities that arise from their intersection with existing property law frameworks. It analyses how property is defined and transferred in the context of NFTs, the implications for creators and acquires, and the best way to protect the latter. By critically assessing these issues, this paper aims to provide some insights regarding the legal principles that should guide the recognition and enforcement of property rights over NFTs, while suggesting new legal paths to accommodate this rapidly evolving technology.
We present a secure and efficient federated learning protocol for autonomous vehicles that resists data leaks, redundancy, and adversarial attacks. Our system combines fast zero-knowledge proofs and compressed Bloom filters to verify updates without exposing private data. Compared to traditional approaches, our method reduces proof sizes by 90 % (under 10 KB), memory by up to 75 %, and maintains accuracy with less than 4% degradation under 30% attack rates. The entire update cycle completes in under 600 ms, making it practical for real-time use in vehicles. This work advances trustworthy AI deployment in dynamic, resource-limited networks.
Waqf is an Islamic economic instrument with significant potential to promote community welfare, yet it continues to face challenges related to transparency, asset certification, and public engagement. The advancement of blockchain technology particularly the use of Non-Fungible Tokens (NFTs) presents an innovative opportunity for the digitalization of waqf, especially as a means of creating unique, transparent, and verifiable asset certifications. This study employs a qualitative approach through a literature review to analyze the potential and challenges of utilizing NFTs within the framework of sharia-compliant digital waqf. The findings indicate that NFTs hold great promise in enhancing efficiency, expanding public participation, and promoting digitally-driven productive waqf models. However, several critical issues remain, including legal uncertainty, the risk of gharar (ambiguity), value volatility, and the lack of supporting infrastructure and digital literacy. This study recommends strengthening regulatory frameworks, developing adaptive fiqh guidelines, and increasing public education to support the implementation of NFTs as a waqf instrument aligned with maqāṣid al-sharī‘ah and capable of delivering broad benefits to the Muslim community.
Smart contracts are self-executing programs that facilitate trustless transactions between multiple parties, most commonly deployed on the Ethereum blockchain. They have become integral to decentralized applications in areas such as voting, digital agreements, and financial systems. However, the immutable and transparent nature of smart contracts makes security vulnerabilities especially critical, as deployed contracts cannot be modified. Security flaws have led to substantial financial losses, underscoring the need for robust verification before deployment. This survey presents a comprehensive review of the state of the art in smart contract security verification, with a focus on Ethereum. We analyze a wide range of verification methods, including static and dynamic analysis, formal verification, and machine learning, and evaluate 62 open-source tools across their detection accuracy, efficiency, and usability. In addition, we highlight emerging trends, challenges, and the need for cross-methodological integration and benchmarking. Our findings aim to guide researchers, developers, and security auditors in selecting and advancing effective verification approaches for building secure and reliable smart contracts.
The integration of blockchain technology into supply chain management represents a paradigm shift in how organizations approach transparency, traceability, and security within procurement processes.This article examines the transformative potential of distributed ledger technology in addressing critical supply chain challenges across various industries in the United States.Through comprehensive analysis of implementation strategies, benefits, and limitations, this research demonstrates how blockchain solutions can revolutionize supply chain operations while enhancing stakeholder confidence and regulatory compliance.The findings reveal significant improvements in data integrity, fraud prevention, and operational efficiency when blockchain technology is properly implemented within existing supply chain frameworks.
With the increasing number of graduates each year, there is a growing need for verified and easily accessible academic certificates. This paper proposes a solution that addresses both verification and accessibility using blockchain technology, with validation as the top priority. Blockchain is a distributed ledger technology that ensures transparency and immutability across a decentralized network. It stores data in blocks, which are cryptographically linked to one another, forming a secure and tamper-proof chain. Due to its decentralized nature, there is no need for a central authority to manage or store the data. Blockchain adheres to the CIA triad—Confidentiality, Integrity, and Availability—while also supporting Authorization, making it a suitable candidate for secure certificate storage and verification. One major advantage of this system is the reduced burden on students to keep physical copies of their certificates, which can be easily lost or damaged. Moreover, in today’s world, creating counterfeit certificates is relatively easy. Blockchain offers a robust solution to this problem due to its immutable and verifiable structure. In this proposed system, when a certificate is issued, a unique hash is generated for it. Along with this hash, a QR (Quick Response) code is also created, linking directly to the certificate stored on the blockchain. By scanning the QR code, anyone can instantly verify the authenticity of the certificate via a secure website. This method is significantly faster and more reliable than traditional manual verification techniques. Blockchain technology presents an ideal solution for sectors that require low-cost, trustworthy, and efficient verification of important documents. Each block in the blockchain is validated through consensus by multiple participants in the network. Once validated, altering a block would require modifying all previous linked blocks and gaining control over more than 50% of the network's nodes—an extremely difficult and nearly impossible task. This ensures the integrity and security of the stored certificates.
Wael Issa, Nour Moustafa, Benjamin Turnbull, Kim‐Kwang Raymond Choo
Sixth-generation (6G) wireless networks are set to transform the Internet of Things (IoT) by enabling faster, smarter, and more connected systems. These networks will bring together a wide range of devices, including cars, robots, industrial machines, and smartphones, to support edge intelligence and real-time decision-making. Federated learning (FL) supports this shift by allowing devices to collaboratively train models without sharing raw data, which helps to protect user privacy. Despite its advantages, FL faces significant security challenges, including poisoning attacks and Byzantine clients, both of which can compromise the training process and degrade the accuracy and reliability of the global model. Although existing methods can detect malicious updates, many advanced attacks still bypass statistical defenses relying on metrics such as median and distance. Thus, developing an FL system that ensures both reliable decision-making and privacy and security guarantees in IoT networks remains a significant challenge. This study introduces a Digital Twin-driven Blockchain-enabled Federated Learning (DT-BFL) framework designed for IoT networks. The framework creates a digital representation of the IoT environment to support secure and decentralized edge intelligence using blockchain and federated learning technologies. DT-BFL is built to detect and filter out potentially poisoned model updates from malicious participants. This is achieved through a new smart contract-enabled decentralized aggregation method called Local Updates Purify (LUP). LUP uses a two-stage filtering process: First, it applies Median Absolute Deviation (MAD) to initially remove outliers, then uses statistical features and clustering to separate honest from malicious updates before aggregating the global model. It also assigns a Trust Score (TS) to each participant based on how much their updates differ from the global model and then uses a genuine criterion to select honest clients by evaluating trust scores, update similarity, and deviation from the global model. Experimental results show that DT-BFL effectively defends against various poisoning attacks on datasets like MNIST, ToN-IoT, and CIFAR-10 using models such as CNN, MLP, ResNet, and DenseNet, and maintains high accuracy even when 50% of the clients are malicious. Using a permissioned blockchain further secures the system by enabling aggregation of the decentralized model and authentication of clients through smart contracts. The source code is available on https://github.com/UNSW-Canberra-2023/LUP .
This project represents a comprehensive digital transformation initiative designed to revolutionize traditional procurement practices through the development of an advanced Smart Tender Management System that enables vendors to seamlessly access complete tender documentation and specifications through a centralized online platform while facilitating efficient electronic bid submission processes. The system fundamentally addresses the inherent inefficiencies and cost burdens associated with conventional tendering methodologies by significantly minimizing additional operational expenses that traditionally encompass extensive advertising campaigns, physical document printing and distribution, manual handling procedures, and administrative overhead costs that often inflate the overall procurement budget. Through its sophisticated digital architecture, the application establishes stringent timeline management protocols that ensure the evaluation process adheres strictly to predetermined schedules and deadlines, thereby eliminating delays that frequently plague traditional tendering systems and compromise project timelines. The platform accommodates multiple vendor participation by providing a robust infrastructure that supports simultaneous bid submissions from diverse suppliers, contractors, and service providers, each presenting unique proposals with varying technical specifications, pricing structures, and implementation methodologies, from which procurement committees can systematically evaluate and select the most suitable proposals based on predetermined criteria including cost-effectiveness, technical merit, vendor credentials, and alignment with organizational objectives. This systematic approach to vendor selection and proposal evaluation has demonstrated significant potential for enhancing organizational profitability through optimized resource allocation, reduced procurement costs, improved vendor competition, and the selection of high-quality solutions that deliver superior value propositions. Furthermore, the implementation of this digital tendering system contributes substantially to improving the overall operational quality and efficiency of organizations by streamlining bureaucratic processes, reducing human error, enhancing transparency and accountability, facilitating better vendor relationships, and providing comprehensive audit trails that support compliance requirements and regulatory standards. The Smart Tender Management System's integration of advanced technologies, including secure document management, automated workflow processes, real-time communication capabilities, and comprehensive reporting mechanisms, positions it as a transformative solution that fundamentally reshapes how organizations approach procurement activities. In essence, this Smart Tender Management System represents a paradigmatic shift from traditional, paper-based, time-consuming procurement practices toward a modern, efficient, technology-driven approach that provides organizations with a powerful, comprehensive tool to systematically streamline their entire tendering ecosystem, significantly reduce operational and financial risks associated with procurement activities, enhance their competitive positioning in increasingly dynamic market environments, and establish sustainable procurement practices that support long-term organizational growth and success while maintaining the highest standards of transparency, efficiency, and stakeholder satisfaction throughout the entire tender lifecycle management process.
The integration of Artificial Intelligence (AI) into decentralized finance (DeFi) has triggered a paradigm shift in the automation and optimization of financial contracts, particularly within the domain of financial derivatives. Derivatives, including options, futures, swaps, and forwards, are among the most complex financial instruments, requiring accurate pricing, efficient settlement, and continuous risk monitoring. Smart contracts—self-executing agreements coded onto blockchain networks—have emerged as a transformative mechanism to automate these processes. However, conventional smart contracts in DeFi are constrained by inefficiencies in execution logic, gas costs, vulnerability to adversarial trading strategies, and limitations in adapting to real-time market fluctuations. This manuscript investigates AI-driven optimization frameworks for smart contracts in derivatives markets, where machine learning algorithms, reinforcement learning agents, and predictive analytics are employed to dynamically enhance pricing mechanisms, counterparty risk management, and execution efficiency. The study builds on an extensive literature review of DeFi, AI-finance integration, and blockchain automation, proposing an AI-augmented smart contract architecture that enables adaptive fee structures, risk-adjusted margin calls, automated dispute resolution, and latency-sensitive derivatives clearing. A simulation-based methodology was employed, where deep reinforcement learning models interacted with synthetic market data to optimize contract logic in futures and options markets deployed on Ethereum Virtual Machine (EVM)-compatible blockchains. Statistical evaluation revealed that AI-enhanced smart contracts demonstrated 25–40% improvement in transaction throughput, 18–25% reduction in gas costs, 30–35% enhancement in derivative pricing accuracy, and 50% reduction in settlement disputes compared to baseline blockchain contracts. The results highlight that AI-driven optimization is not only feasible but essential for scaling derivatives trading in DeFi to institutional-grade levels. The paper concludes by discussing regulatory implications, computational limitations, adversarial AI threats, and the future trajectory of autonomous financial engineering.
Predicting the price of Bitcoin is crucial, primarily because of the market’s rapid volatility and non-linear environment. For enhanced prediction of the price of Bitcoin, this research proposed a novel interpretable hybrid technique that combines long short-term memory (LSTM) networks with convolutional neural networks (CNN). Deep variational autoencoders (VAE) are used in the stage of preprocessing to determine noticeable patterns in datasets by learning features from historical Bitcoin price data. The CNN-LSTM model additionally implies Shapley additive explanations (SHAP) to promote interpretability and clarify the role of various features. For better performance, the methodology used data cleaning, preprocessing, and effective machine-learning techniques. The hybrid CNN + LSTM model, in collaboration with VAE, obtains a mean squared Error (MSE) of 0.0002, a mean absolute error (MAE) of 0.008, and an R-squared (R2) of 0.99, based on the experimental results. These results show that the proposed model is a good financial forecast method since it effectively reflects the complex dynamics of primary changes in the price of Bitcoin. The combination of deep learning and explainable artificial intelligence improves predictive accuracy as well as transparency, thus qualifying the model as highly useful for investors and analysts.
Karim Moawad, Ammar Hummieda, Ahmad Musamih, Khaled Salah · 5 authors
Lithium-ion batteries (LIBs) have become a cornerstone of modern technology, where they serve as the power source for a wide range of applications, including electric vehicles and renewable energy storage systems. However, rapid production growth has introduced challenges regarding end-of-life management, particularly with waste disposal, resource recovery, and environmental sustainability. Inefficient recycling often leads to valuable materials like cobalt, lithium, and nickel being discarded in landfills, which exacerbates resource scarcity and poses environmental and health risks. To address these issues, there is a critical need for more efficient, transparent, and accountable systems for the collection, recovery, and recycling of LIBs. In this paper,A blockchain and Non-Fungible Token (NFT)-based solution is proposed to enable circular recycling and material recovery. This system improves transparency, traceability, and accountability throughout the battery lifecycle. The smart contracts (SCs) source code is made publicly available on GitHub.
This study investigates the integration of the Portuguese Citizen’s Card authentication with Hyperledger Fabric blockchain technology, addressing the challenge of bridging traditional government-issued digital identities with blockchain-based systems, particularly focusing on reducing barriers to Web3 adoption for users unfamiliar with decentralized technologies. The proposed solution leverages the Autenticação.gov Software Development Kit (SDK), developed by the Portuguese Agency for Administrative Modernization (AMA) to create a secure bridge between the Citizen’s Card authentication system and Hyperledger Fabric's permissioned blockchain framework. The study examines how this approach can facilitate the development of transparent, tamper-proof authentication systems suitable for critical applications such as e-voting and digital government services, and also feasible for on-premises systems. The findings suggest that integrating existing digital identity systems with blockchain technology can promote wider acceptance of decentralized solutions while maintaining security, privacy, and accessibility standards required for public sector applications.
As data increasingly becomes a key factor of production for artificial intelligence (AI), this paper proposes a blockchain-enabled, decentralized AI data-market framework. To address the long-standing problems of low transparency, high privacy risk, and misaligned incentives in traditional data trading, we design a layered hybrid consensus that combines Proof of Stake (PoS) with Practical Byzantine Fault Tolerance (PBFT), balancing economic security with sub-second finality. A token-based incentive model that weights data quality, volume, and staking risk is introduced to couple value discovery with the suppression of low-quality data. By combining symmetric encryption with proxy re-encryption, the framework allows data to be “usable yet invisible” while exposing a compliance interface for regulated auditability. A prototype deployed on 18 nodes achieves 4,750 tx · s⁻¹ throughput and 148 ms latency, with energy consumption far below Proof-of-Work (PoW) schemes—demonstrating performance, privacy, and ESG friendliness. This work provides a reproducible technical path and theoretical foundation for sustainable innovation in data-factor circulation and AI applications.
Federico Cernera, Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini · 5 authors
In the world of cryptocurrencies, the public listing of a new token often generates significant hype. In many cases, the price of the token skyrockets in a few seconds, and timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify operations performed using sniper bots. Our findings reveal 352,413 sniping operations on Ethereum and 1,716,917 on BSC for a total turnaround of $155,630,184 and $137,548,859, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze possible countermeasures and mechanisms used in token smart contracts that can reduce the negative impact of sniper bots.
Non-fungible tokens (NFTs) have the potential to serve as fiduciary collateral in Indonesia. As a blockchain-based innovation, NFTs enable the unique representation and transfer of digital asset ownership. Under Indonesia’s Fiduciary Security Law, NFTs qualify as fiduciary collateral objects since they are classified as intangible assets. This study examines copyright protection for NFTs in the context of fiduciary collateral, along with the legal and technical challenges in their implementation. Key obstacles include the lack of specific regulatory frameworks, insufficient blockchain infrastructure, and limited public understanding of NFTs as fiduciary collateral. Consequently, there is a need for comprehensive regulations and the establishment of oversight institutions to ensure transactional legality and security.Such regulatory measures are expected to facilitate the use of NFTs as fiduciary collateral, enhance public trust, and promote the growth of a blockchain-based digital ecosystem in Indonesia.