Current blockchain research and analytics tend to prioritize observable on-chain transactions, obscuring the processes through which cryptocurrencies are created, publicised, retained, and disposed of. In response, this paper considers distributed ledger technologies from records management principles in ISO 15489-1:2016. Setting off by specifying the parallels -- that is transactions as "records", crypto-asset units as "information assets", and blockchains as "aggregations" -- we introduce a seven-stage lifecycle for blockchain data. We apply the framework to Bitcoin, a fungible token, and a non-fungible token. On this basis, we argue that blockchain systems are not merely transactional infrastructures but record management systems with distinctive characteristics. We discuss how the on-chain/off-chain boundary and privacy-enhancing technologies can complicate lifecycle visibility, with particular relevance for crypto-crime research and investigation. As a meta-level framework, the lifecycle perspective enables positioning existing research, decomposing legal, regulatory, technological, and operational challenges by stage, and informing lifecycle-aware approaches to blockchain governance, analytics, and regulation.
The rapid expansion of the digital financial assets (DFA) market in Russia offers new opportunities for market participants while simultaneously creating fresh challenges and risks of financial crimes. The author examines the economic and legal nature of digital rights within the context of Federal Law No. 259-FZ and assesses the effectiveness of current regulations. An analysis of recent judicial and market practices reveals specific predicate and direct risks to the anti-money laundering system, including “controlled defaults” by issuers, fraud, and the emergence of Ponzi schemes. The article also highlights the lack of standardized smart contracts in this market, which complicates the verification of distributed ledger algorithms. Current threats associated with the use of generative artificial intelligence for creating “money mules” and synthetic identity fraud are identified. Based on a comparison of Russian experience with the regulatory approaches of the USA and Thailand, the necessity of forming a proactive legal environment is justified. Recommendations include the need to align regulatory regimes for traditional and digital financial assets and to enhance the professional qualifications of the judiciary.
The rapid expansion of cryptocurrency markets has fundamentally transformed the global financial system and challenged traditional approaches to financial regulation. Cryptocurrency exchanges have emerged as key intermediaries facilitating the purchase, sale, transfer, and storage of digital assets across jurisdictions. However, the borderless and decentralized nature of cryptocurrencies has generated significant legal concerns relating to anti-money laundering compliance, counter-terrorist financing measures, consumer protection, taxation, cybersecurity, market manipulation, and regulatory enforcement. This article examines international legal frameworks governing cryptocurrency exchanges, analyzes regulatory approaches adopted by leading jurisdictions, including the European Union and the United States, and evaluates major enforcement actions involving Binance and FTX. The study further explores emerging challenges associated with decentralized finance (DeFi) and proposes recommendations aimed at strengthening international cooperation and harmonizing legal standards for digital asset regulation.
Olha Kovalchuk, Ruslan Shevchuk, Serhiy Banakh, N. P. Holota · 6 authors
Abstract This study examines the relationships between national cryptocurrency regulation, anti-money laundering (AML) risks, and decentralized finance (DeFi) adoption across global jurisdictions. Using correspondence analysis, correlation techniques, and regression modeling with control variables, we analyze data from the Basel AML Index and Retail DeFi Rankings to identify structural patterns in the interaction between regulatory frameworks, institutional quality, and digital asset ecosystems. The results reveal a counterintuitive global distribution in which advanced economies with strong regulatory regimes and low AML risks tend to exhibit limited retail DeFi activity, whereas jurisdictions characterized by weaker institutions and higher money laundering risks show significantly higher levels of DeFi usage. Further, the correspondence analysis identifies three distinct clusters of countries defined by specific configurations of regulatory approaches, AML effectiveness, and DeFi adoption, indicating that these relationships are configurational rather than purely linear. Robustness checks demonstrate that qualitative features of regulatory regimes are more strongly associated with DeFi adoption than conventional quantitative indicators of economic development or governance quality, thereby distinguishing DeFi diffusion from broader cryptocurrency usage dynamics. Mediation analysis provides partial support for a compensatory pattern: financial inclusion is a significant negative predictor of DeFi adoption, though a statistically confirmed mediation pathway between AML risk and DeFi activity through financial exclusion was not established. The study also highlights substantial global regulatory fragmentation, with 57% of jurisdictions classified as “Undecided” or “Improving,” underscoring the ongoing difficulty of reconciling financial innovation with stability and risk mitigation. These findings provide evidence-based guidance for policymakers designing adaptive regulatory frameworks and establish a foundation for further research on the evolution of digital finance regulation.
Andreas Polyvios Delladetsimas, Elias Iosif, Stamatis Papangelou, George Giaglis
This article examines blockchain as an enabling technological component for data management tasks that are independent of currency-related functionality, a less-discussed aspect of a technology commonly associated with cryptocurrencies and decentralized finance (DeFi). Drawing on empirical findings from the DIGI4ECO project as a case study, we present a structured literature review and cross-domain analysis of blockchain-based data management systems (BDMSs), examine a representative permissioned BDMS implementation, and synthesize practical design guidelines and implementation insights for BDMS development. This perspective is motivated by core blockchain properties such as immutability and transparency, as well as by the observation that existing resources for BDMS development, including methods, tools, and best practices, remain fragmented and less developed than those available for more mature technologies.
Many blockchain-based cyber threat intelligence (CTI) sharing systems emphasize immutability and auditability, but often treat CTI submissions as ordinary blockchain transactions without explicitly separating content validation from publication anchoring. This paper presents CTIB, a proof-of-concept hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) framework for CTI publication. CTIB uses a sequential workflow in which a PoS committee first evaluates CTI submissions, and an accepted feed hash is then anchored through a PoW step to provide verifiable temporal binding. The prototype is evaluated in a controlled local Hardhat environment; therefore, the results should be interpreted as prototype-level feasibility evidence rather than production-scale deployment results. CTI content is represented using STIX 2.1, canonicalized, and hashed using SHA-256; only integrity-critical evidence is stored on-chain, while full CTI content remains off-chain. Experimental results demonstrate prototype-level feasibility, with measured throughput, latency, and success rate metrics under different PoW difficulty profiles. Across ten independent local runs, CTIB achieved an average throughput between 141.13 and 166.14 feeds/min, average p50 latency between 326.18 and 403.09 ms, and average p95 latency between 553.22 and 700.82 ms under the tested difficulty profiles. Security analysis uses analytical modeling, committee capture probability, and Monte Carlo simulation to evaluate majority-attack feasibility under stated assumptions. The results indicate that sequential compromise of both validation and anchoring layers increases the cost of coordinated manipulation.
Eunchan Park, Kyonghwa Song, Won Hoi Kim, Wonho Song · 5 authors
Traditional blockchain untraceability schemes, such as mixers and privacy coins, obscure the sender-receiver relationship by placing transfers within an anonymity set. This paper studies a stronger goal: whether the transfer event itself can be made unobservable by blending into common decentralized-finance (DeFi) activity. We introduce Deniable Covert Asset Transfer (DCAT), a class of transfers that stage common loss-producing events, such as sandwich and arbitrage operations, so that a sender appears to suffer an ordinary loss while the receiver appears to profit from it. We design and validate two DCAT instantiations: a sandwich-based transfer on Ethereum and an arbitrage-based transfer on Arbitrum. Our experiments show that, under the evaluated settings, DCAT transfers are empirically unobservable on both chains. They are syntactically identical to corresponding maximal extractable value (MEV) activities, classified as ordinary extractions by standard MEV detection tools, and leave the sender and receiver unlinked under representative forensic tools. Since syntactic inspection cannot distinguish DCAT from ordinary MEV activity, we examine whether economic semantics provide useful forensic signals. Through a large-scale study of MEV losses on Ethereum and Arbitrum, we show that key semantic features follow power laws. Extreme losses and repeatedly exploited addresses occur in the wild, and thus are not by themselves definitive evidence of collusion. This gives staged transfers plausible deniability and makes fixed-threshold detection prone to false positives. We therefore develop a multivariate statistical method for forensic triage that ranks incidents by the joint rarity of their economic footprint. Applied to real-world DeFi activity, our method narrows a large search space to suspicious cases for manual investigation; we present three such cases to illustrate this prioritization.
In this paper, we examine the patterns and determinants of cross-border cryptocurrency flows. While our analysis focuses primarily on Bitcoin flows, the cryptocurrency with the largest market capitalization, we show that our key results also extend to four major stablecoins. After documenting global patterns of cross-border Bitcoin flows and contrasting them with those of traditional capital flows, we employ a cross-country panel approach to identify the key drivers of cross-border crypto flows for up to 162 countries. Our results provide evidence for the presence of multiple coexisting motives. The most significant motives comprise strategies to adjust to unfavorable macro and financial developments, as well as the need to conduct international payment and remittance transfers. Moreover, by conducting a case study of cross-border Bitcoin flows after the COVID-19 shock, we find that these motives were particularly relevant at a time when economic conditions were weak and the need for remittances appeared high. Gaining a better understanding of the motives behind cross-border cryptocurrency transactions is crucial for informing the public debate on cryptocurrencies and their potential use cases.
Part II, 'How Decentralised Security is Organised,’ examines the actors, infrastructures, and incentives that shape security practices in Web3—from the structural insecurity of digital infrastructure to the emergent role of white hat hackers and collaborative security initiatives they coordinate. This chapter introduces a new protagonist in the security landscape: the blockchain white hat hacker. Far from operating in the shadows, this actors play a vital role in the moral, political, and economic landscape of blockchains by helping to safeguard decentralised systems. This chapter examines the practices, motivations and incentives—both financial, moral, and reputational—that drive white hat activity, highlighting how these individuals contribute to vulnerability disclosure, incident response and the overall resilience of the blockchain ecosystem. In doing so, it situates white hats not as central figures in the evolving ecosystem of decentralised security governance.
The increasing complexity of global financial systems has necessitated the adoption of more efficient and transparent mechanisms for combating money laundering (AML). Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a promising solution to address the limitations of traditional AML systems. This paper represents a review, exploring the potential applications of AI and blockchain in enhancing financial control systems, in particular, within AML compliance, focusing on key areas such as transaction monitoring, cross-institutional data sharing, and regulatory reporting. The integration of blockchain can streamline AML processes, reduce operational costs, and increase the effectiveness of detecting illicit financial activity. The combination of blockchain technologies and artificial intelligence algorithms in financial control is considered. It is shown how automation of transaction analysis can strengthen the stability of the banking system and prevent financial crimes. It is demonstrated that the convergence of Artificial Intelligence and blockchain technologies presents a transformative opportunity to strengthen AML frameworks, particularly in the face of rising crypto-enabled financial crimes. This research offers several important contributions to the academic literature. First, it presents a synthesis of the current status of artificial intelligence approaches used for compliance in detecting fraud in Bitcoin transactions. This review discusses the essential methodologies and tactics in a particular area that intersects finance and compliance but falls under the broader disciplines of AI-driven finance and decentralized finance (DeFi). The incorporation of AI into financial control marks a tremendous technological revolution that is affecting industries across the board. Second, the study assesses the current state of the publications, major trends, and research gaps, emphasizing areas that deserve additional investigation.
Bitcoin's security posture depends on more than consensus rules and proposal-based governance. This study proposes a formal cybersecurity framework for Bitcoin that integrates automated vulnerability monitoring, dependency inventory control, exploitability analysis, relational graph analytics, and funded bounty incentives. The framework is intended to reduce vulnerability exposure in Bitcoin Core and adjacent open-source dependencies through Dependabot for automated security pull requests, FOSSA for supply chain visibility, CycloneDX for exploitability classification, and Neo4j for relational tracking of vulnerability scope. The study argues that Bitcoin Improvement Proposals are insufficient for vulnerability management because they do not provide rapid remediation workflows or structured researcher incentives. A formal responsibility model is also proposed to assign accountability across maintainers, contributors, bounty hunters, financers, and policy functions. The resulting framework advances a more responsive, measurable, and auditable security posture for the Bitcoin ecosystem than existing frameworks.
Phishing attacks pose a significant security issue in Ethereum-based blockchain systems. Existing solutions, like TEGDetector, address these attacks by analysing how transactions evolve over time using Transaction Evolution Graphs (TEGs) constructed via time slicing, followed by a dynamic graph classifier that captures both spatial structure and temporal evolution with learned time coefficients. However, building and managing these graphs across multiple stages makes the overall approach complex and difficult to implement. In this work, we propose E2E-EmbedDetector, a lightweight end-to-end neural classification model that works directly with raw transaction data. The model learns embedding representations for important entities such as From, To, and ContractAddress, and also used two additional numeric features: transactional value and a derived input length. We train and evaluate the model on a balanced dataset of 50,000 Ethereum transaction using an 80/20 stratified split. The model achieves an accuracy of 95.63%, precision of 0.9265, recall of 0.9912, an F1 score of 0.9578, a ROC-AUC score of 0.9915 and a PR-AUC score of 0.9909. These results show that strong phishing can be achieved using a simpler and more practical tabular approach, without relying on complex temporal graph- based networks.
<p>It seems that humans have gone too far in creating technological industries; it also seems that they have lost control over the ethics of developing their various tools. If the legal system in the past faced a major challenge in protecting the rights and interests of those dealing with the Internet from the misuse of information-technology tools, it now faces a new challenge after Mark Zuckerberg&rsquo;s announcement (owner of Facebook, which changed its name to &ldquo;Meta Platforms&rdquo;) of his commitment to developing the virtual world he called &ldquo;Metaverse&rdquo; &ndash; a world that blends reality and fantasy, allowing users to interact with one another with real emotions in three-dimensional virtual environments completely similar to reality. This paper aims to define the nature of metaverse technology and study the extent to which criminal laws are prepared to face the challenges it poses, especially with the emergence of criminal activities through it. To this end, the research followed a descriptive and analytical approach in presenting the topic and was divided into three parts: Firstly, explaining the nature of metaverse technology. Secondly, Describing examples of attacks in this environment. Lastly, Addressing the extent to which criminal laws are prepared to face the challenges of metaverse technology. The research concluded that the metaverse is a recent phenomenon in the cyberspace environment, which began through the electronic-gaming portal and has become a business environment in various fields through blockchain platforms and non-fungible tokens (NFTs). Avatars are a form of personal data that identify users for access to the metaverse and may themselves become the subject or tool of criminal activities. The paper also found that the danger lies in the uncontrolled acceleration of technological development. The metaverse, without legal safeguards, is fertile ground for numerous crimes. Criminal activities in the metaverse are modeled on cybercrimes given their nature and characteristics. Consequently, the research recommends that legislators review substantive and procedural laws to ensure effective responses at both international and national levels, to adopt the idea of electronic legal personality, and to refrain from rushing to introduce practices and services in this environment until clear legal frameworks define obligations and responsibilities.</p>
Open access
Cybercrime and Law Enforcement Studies
Law, AI, and Intellectual Property
Legal, Health, Environmental and COVID-19 Challenges
The quick proliferation of cryptocurrency markets has essentially transformed the financial frameworks of the globe in that it has made it possible to initiate the means of value transfer across borders that are decentralized, borderless, and technologically advanced. Cryptocurrencies are based on blockchain and cryptographic protocols and enable peer-to-peer transactions without use of traditional financial intermediaries, which improves efficiency, lowers the costs of transactions, and increases financial inclusion, especially in underserved areas. In addition to payments, the technologies have stimulated innovation in fields like decentralised finance, smart contracts, and systems of digital identity. But the very same characteristics which render cryptocurrencies appealing also pose serious threats to regulation and law enforcement. The anonymity of transactions combined with the decentralized and cross-border structure of blockchain networks make it difficult to determine who the users are and apply jurisdiction-specific legislation. As a result, cryptocurrencies have become more and more related to different types of financial crime such as money laundering, terrorist financing, tax evasion, ransomware attacks, and illegal trading in darknet markets. The paper discusses the principal types of crime in the context of cryptocurrency and evaluates the challenges encountered by regulatory bodies and law enforcement agencies that might need to overcome these challenges. It also discusses the international regulation reaction, including the involvement of the international standard-setting organizations and the development of compliance systems, including anti-money laundering (AML) and know-your-customer (KYC) systems. Furthermore, the paper also mentions that technological solutions, such as blockchain analytics, are increasingly gaining significance in enhancing investigative potential. The paper concludes that, regardless of the revolutionary potential of cryptocurrencies in terms of financial innovation, their productive regulation involves a moderate and coordinated strategy, incorporating legal and regulatory models, technological progress, and global collaboration.
Money laundering enables malicious actors to integrate illegal profits into the legitimate economy and has long been a central concern in financial regulation. Blockchain systems introduce new channels for laundering through decentralized, pseudonymous, and cross-border asset transfers. In this context, blockchain exploiters often rely on laundering to conceal fund origins and enable cash-out.
Sreekanth Vp, Ronny Sebastian Roy, Surya Gopalakrishnan, Thanmaya S Krishna
The proliferation of cyber threats and the increasing digital vulnerability of students necessitate innovative educational approaches to cybersecurity awareness. Traditional methods of teaching cybersecurity often fail to engage learners effectively, resulting in poor knowledge retention and limited practical skills. This paper introduces Zyberix, an interactive educational game that leverages Web3 technologies to teach cybersecurity concepts through immersive gameplay and blockchain-based incentives. By integrating Unity game engine with Ethereum smart contracts, IPFS decentralized storage, and NFT rewards, Zyberix creates an engaging learning environment that simulates realistic cyber threats including phishing, malware, and social engineering attacks. The system rewards successful challenge completion with verifiable Non-Fungible Tokens (NFTs), providing both motivation and an introduction to blockchain technology. This research demonstrates how gamified experiential learning combined with Web3 technologies can significantly enhance cybersecurity education, addressing the critical gap between theoretical knowledge and practical application while fostering digital literacy among students.
Purpose: Cyber fraud and money laundering are growing threats to the integrity of operations in the Nigerian banking sector, which undercuts the confidence of customers. This study examined the influence of FinTech solutions specifically smart contracts and cryptographic security on fraud prevention in Nigerian deposit money banks (DMBs), in view of the increasing incidence of cyber fraud and money laundering in the sector. Methodology: The study adopted a quantitative research design, underpinned by the Technology Acceptance Model (TAM), agency theory, and control theory. A cross-sectional survey was conducted on 312 management and IT employees drawn from five selected DMBs in Lagos State. Data collected were analyzed using descriptive statistics and multiple regression analysis. Results and conclusion: The findings revealed that smart contracts have a positive and statistically significant effect on the prevention of cyber fraud (r = 0.408, p < 0.001), while cryptographic security exerts a strong and significant influence on the prevention of money laundering (r = 0.433, p < 0.001). The study concluded that these FinTech solutions are effective tools for enhancing fraud prevention and improving the security architecture of Nigerian banks. Implication of findings: The study implies that deposit money banks should prioritize investment in FinTech innovations, while regulatory authorities should establish supportive frameworks to facilitate their adoption, thereby strengthening financial security and restoring customer confidence in the banking system.
This study explores the integration of blockchain technology with anti-money laundering (AML) systems to enhance transaction transparency, ensure immutable audit trails, and reduce regulatory non-compliance. Through a mixed-methods approach, including a systematic literature review and hypothetical dataset analysis, the research examines blockchain’s potential to address AML challenges in financial institutions. Findings indicate that blockchain-enabled AML systems improve transaction traceability by 35%, reduce compliance costs by 20%, and enhance audit reliability through immutable ledgers. However, scalability and regulatory harmonization remain barriers. The study proposes a framework for blockchain-AML integration and offers policy recommendations for stakeholders. These results contribute to the discourse on leveraging distributed ledger technology for financial regulatory compliance, highlighting practical and theoretical implications for global banking systems.
Adegboyega Afolabi, Modupe M. Adesemowo, Olayemi O. Amosun, M. Olamide Otuyelu · 6 authors
As digital intermediation accelerates, Nigerian deposit money banks (DMBs) confront rising cyber-enabled fraud since the launch of Bitcoin in 2009, despite ongoing reforms. Most blockchain research still centres on cryptocurrencies, with relatively few studies examining their applications in other industries. This study investigates whether blockchain technology (smart contracts, permissioned distributed ledgers, and secure digital wallets) is associated with lower fraud in Nigerian DMBs.Using survey data from 120 bankers across five institutions spanning international, national, and regional licenses, we estimate Ordinary Least Squares (OLS) models relating each BCT dimension, and a composite index, to two outcomes: spread of fraud (SOF) and internet fraud activities (IFA). Reliability analysis shows strong internal consistency (α = 0.75–0.91). Models include robustness checks for multicollinearity and specification. Results indicate that higher perceived deployment of smart contracts, distributed ledger, and digital wallet capabilities is negatively and significantly associated with SOF and IFA; a composite BCT index positively predicts overall fraud-reduction assessments. These findings align with recent sectoral evidence that blockchain adoption lowers fraud-related costs and enhances transaction integrity in banking. Given Nigeria’s elevated incidence of electronic fraud in retail payments, the practical implication is that embedding programmable controls, tamper-evident shared records, and cryptographic authentication can harden high-risk processes. We recommend that regulators and DMBs advance permissioned BCT pilots integrated with Anti-Money Laundering (AML) and Know Your Customer (KYC) workflows, strengthen reporting standards, and build human-capital readiness. Beyond cryptocurrency, enterprise-grade BCT offers credible pathways to reduce fraud externalities and improve operational resilience in Nigeria’s banking sector. Keywords: Blockchain; Smart contracts; Distributed ledger; Digital wallet; Bank fraud; Nigeria.
Money laundering in cryptocurrency networks poses persistent challenges for financial intelligence units due to the pseudo-anonymous architecture of blockchain systems and the limited effectiveness of conventional rule-based detection methods. This study introduces chaos theory and recurrence quantification analysis (RQA) as a novel framework for characterizing temporal behavioral dynamics in Bitcoin money laundering transactions. Analyzing 46,564 labeled transactions from the Elliptic Bitcoin Dataset spanning 2009-2018, we construct aggregate time series for illicit and licit transaction volumes across 49 discrete temporal steps, corresponding to the dataset’s inherent graph-based snapshot structure, and apply phase space reconstruction techniques to compute three RQA metrics: determinism (DET), laminarity (LAM), and entropy (ENTR). Results reveal paradoxically higher determinism in illicit transactions (38.24% vs. 16.67% for licit), substantially elevated laminarity (35.80% vs. 0.00%), and greater entropy (0.45 vs. 0.00%), indicating that sophisticated obfuscation strategies inadvertently introduce detectable deterministic signatures. Augmenting conventional graph-based features with RQA metrics significantly enhances Random Forest classification performance, reaching near-optimal levels (F1 = 1.000, AUC = 1.000) within the evaluated dataset environment, with entropy emerging as the single most discriminative predictor. While these exceptional results reflect the high fidelity of chaos-based features in capturing structured laundering patterns from this period, they serve as a benchmark for the theoretical potential of nonlinear analysis in blockchain forensics. These findings demonstrate that temporal complexity features offer a powerful diagnostic tool for real-time monitoring and detection of systemic financial crime in evolving cryptocurrency ecosystems.
This working paper examines a structural shift in search behaviour affecting crypto and Web3 brands: the divergence between AI-summarised retrieval (zero-click) and deep-intent click-through traffic. Drawing on published zero-click search data and observed AI citation patterns, it identifies why high-volume content strategies are failing in AI-mediated search environments and outlines three asset classes - statistics hubs, original industry research, and high-utility tools - that generate the editorial backlink profiles AI models use to select citation sources. The paper defines the concept of "authority infrastructure" as a capital investment in linkable assets with compounding residual value, contrasting this with recurring spend on keyword-optimised content with no durable equity. Intended for crypto protocol marketing teams, Web3 founders, and DeFi growth leads evaluating content strategy for AI search visibility. Published by David Wood, CryptoContent.dev.
Semaria Eva Elita Girsang, Shaumiwaty, Muhammad Noval Aryansah, Mario Putra Sanjaya · 5 authors
The development of blockchain technology has driven the emergence of Non Fungible Tokens (NFTs) as unique digital assets traded through specialized marketplaces, forming a new digital economic ecosystem. Despite the rapid growth of the NFTs market, issues such as price volatility, the dominance of speculative activities, and uncertainty regarding long-term economic value remain insufficiently understood in academic studies. This research aims to analyze the role of NFTs marketplaces in shaping the economic value of digital assets, identify the factors influencing NFTs price dynamics, and evaluate the economic implications of the NFTs market for creators, investors, and marketplace platforms. This study employs an empirical quantitative approach by utilizing NFTs transaction data obtained from the OpenSea API, NonFungible.com, and CryptoSlam. The variables analyzed include NFTs prices, trading volume, liquidity, creator reputation, rarity score, and asset category. Data analysis is conducted using statistical and econometric methods to identify price determinants and market dynamics. The results indicate that NFTs values are significantly influenced by scarcity levels, creator reputation, asset utility, and the visibility provided by marketplaces. Marketplaces play a crucial role in shaping liquidity and market expectations, but they also contribute to increased volatility and speculative tendencies. This study concludes that the NFTs market has the potential to generate real economic value, yet it continues to face risks related to speculation and instability. These findings contribute theoretically to the digital economics literature and provide practical implications for the development of a more sustainable NFTs ecosystem.
Blockchain technology is a somewhat new approach to finding the integrity and chain of digital evidence in various industries, including law enforcement, forensic investigations, supply chain management, and judicial proceedings. Although traditional evidence-keeping systems are prone to manipulation, loss, and inefficiency, blockchain offers an immutable, transparent, and decentralized ledger that securely records and validates every evidence-related transaction. Blockchain technology increases reliability in handling both physical and digital evidence. It uses distributed consensus, intelligent contracts, and cryptographic hashing to eliminate human error and backdoor intervention by assuring immutability, accountability, and automation. This study offers a model blockchain (Chain of Digital Evidence) based on the Ethereum blockchain to guarantee integrity and authenticity in the chain of digital evidence. Ethereum&s;s decentralization ensures that digital evidence is free from manipulation, transparent, and easily verifiable. The study discusses other challenges and prospects for integrating the Ethereum blockchain into the digital evidence chain.