We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule. Trained on 6,034 fully-filled hourly Binance Options surfaces of BTC and ETH spanning May-October 2023 and parameterised on a common $6 \times 7$ tenor-delta grid, the model attains a hidden-cell surface-completion RMSE in the 0.94-1.56 vol-point range across both markets and mask rates 10-50%. The hybrid predictor attains 0.83 vol points at 50% masking against 7.00 for the smile re-fit alone, an eightfold reduction obtained at no additional inference cost. Under structurally-correlated hole patterns that emulate the withdrawal of an entire tenor of strikes, the smile re-fit incurs 9.6-13.1 vol points of error while the learned model remains at 1.5-1.9, isolating a regime in which the generative model is the only viable predictor. Joint training on BTC and ETH improves the in-distribution model on both markets by 9-27% relative to the better-performing single-symbol counterpart, indicating a substantially shared vol-surface manifold across the two largest cryptocurrencies over the observation window. The hybrid is calendar- and butterfly-arbitrage-free at the listed strikes, a property that the parametric smile re-fit alone fails at high mask rates. The per-snapshot reconstruction error of the trained model flags the late-October ETF-anticipation rally and the August $17$, $2023$ flash crash as elevated-error periods without supervision. All training and evaluation infrastructure is released to support reproducible follow-on work.
Cryptocurrency markets are prone to violent, synchronised drawdowns, challenging the claim that a basket of crypto-assets offers genuine internal diversification. Because standard covariance-based metrics fail to capture asymptotic tail dependence, they systematically understate systemic risk and overstate diversification benefits precisely when markets crash. This study maps the conditional dependence structure of the cryptocurrency market directly in the joint tails, isolating direct extremal linkages from those mediated by the rest of the system. We analyse the daily returns of the thirteen largest cryptocurrencies over a sequence of 89 overlapping windows spanning late 2021 to 2025. We apply dynamic Hüsler-Reiss graphical models of extremes, estimated separately for joint crashes and rallies, and benchmark them against a Gaussian graphical model of ordinary co-movement. The results reveal a near-complete and stable lower-tail graph, an upper tail that thins over time to re-form sectoral structures, and the dissolution of ordinary token categories into a single block anchored by a Bitcoin-Ethereum core. These findings imply that intra-crypto diversification fails on the downside, standard risk models underestimate market-wide crash probabilities by roughly eight-fold, and dynamic extremal graphs offer a superior tool for systemic risk monitoring.
Data availability is a fundamental bottleneck in modern blockchain networks. Most blockchain systems rely on a full-replication model, which requires downloading of a full block to verify its availability. This model does not scale with block size because every node must handle large volumes of data, leading to slower block propagation, duplicated data transfer, and longer consensus agreement. This issue is well-known in Ethereum, where layer-2 rollups publish data directly into the chain. To overcome, Ethereum adopts Data Availability Sampling (DAS) to let nodes keep only a small fragment of the data while still ensuring availability. Prior work on DAS has focused on cryptographic foundations. Meanwhile, the peer-to-peer network layer that provides Byzantine-tolerant and scalable mechanisms for discovery and routing of DAS fragments is underexplored. We propose CDA, a new design for DAS based on coded distributed arrays that leverages network coding to ensure both robustness and efficiency. Our evaluation study compares CDA to RDA, the latest DAS development of Ethereum, showing an improvement of several times better.
Dustin Weiss, Robert Gaudiosi, Z. Ivy Zhou, Robert I. Webb
This paper examines intraday Bitcoin spot returns and trading activity around the expiration of Deribit Bitcoin options. Using data from spot exchanges and Deribit perpetual futures, we document a statistically and economically significant return reversal around expiration. The effect concentrates on days with elevated at-the-money open interest and is strongest when cumulative gamma exposure is negative, which is consistent with positive feedback trading pressure induced by option market makers hedging net short exposure. Trading activity also rises around expiry in Deribit perpetual futures and in the spot exchanges used to determine the Deribit settlement price. These intraday price effects are economically meaningful, implying annual wealth transfers of approximately USD 50 million between option writers and holders. Overall, the findings highlight the role of daily option expirations in shaping short-horizon price formation in Bitcoin markets and have implications for regulated investment products that rely on spot-market reference prices.
A. B. Hajira Be A. B. Hajira Be, S.Bhuvaneshwari S.Bhuvaneshwari, Sankari.S Sankari.S
Cryptocurrency markets have gained significant global attention due to their decentralized nature and high financial value. Among various cryptocurrencies, Bitcoin is the most widely traded and exhibits highly volatile price behavior. Accurate analysis and prediction of Bitcoin price trends are challenging because the market is influenced by rapid trading activities, large data streams, and complex temporal patterns. This paper presents a streaming data collection and analysis system for Bitcoin using the Long Short-Term Memory (LSTM) deep learning algorithm. The proposed system continuously collects real-time Bitcoin market data from online cryptocurrency exchanges through streaming APIs. The collected data is then preprocessed and analyzed using an LSTM-based predictive model capable of learning long-term dependencies in time-series data. The LSTM network processes sequential historical price data to forecast future market trends and provide analytical insights into Bitcoin price movements. The system integrates data acquisition, preprocessing, deep learning-based prediction, and visualization modules to create an efficient cryptocurrency analysis framework. The proposed approach focuses on improving prediction accuracy by combining real-time streaming data with advanced neural network models. This system can assist researchers, financial analysts, and investors in understanding cryptocurrency market behavior and making informed trading decisions. The proposed design demonstrates the feasibility of integrating streaming data technologies with deep learning models for real-time financial market analysis. Keywords— Cryptocurrency, Bitcoin, Streaming Data, LSTM Algorithm, Deep Learning, Time-Series Prediction, Financial Data Analysis.
This paper develops a mathematical framework for modeling Bitcoin price dynamics through a system of coupled stochastic differential equations (SDEs). We capture the complex nonlinear interactions between Bitcoin price and five key factors: investor sentiment, trading volume, mining hashrate, transaction fees, and transaction counts. The model incorporates jump processes to account for sudden price movements and regime-switching to capture state-dependent dynamics. We derive the resulting partial differential equations for derivative pricing and analyze the system's behavior through simulation. Our empirical findings suggest significant feedback mechanisms between network metrics and price dynamics, with hashrate exhibiting the strongest correlation with price movements. The framework provides a foundation for understanding the complex, non-linear, and fractal-like behavior observed in cryptocurrency markets while enabling the pricing of derivatives in this emerging asset class.
Berna Aydoğan, Gülin Vardar, Işık Özge Yumurtacı Hüseyinoğlu, Gizem Halil Utma
Purpose This study examines the determinants of cryptocurrency participation through the lens of social cognitive theory (SCT hereafter), investigating how cognitive, behavioral, and environmental dimensions, including financial literacy, trust and environmental awareness, influence investment behavior. Design/methodology/approach Survey data from 441 adults were analyzed using hierarchical logistic regression models for both the full sample and a subsample of individuals with current, former or intended cryptocurrency investment. Findings Objective financial literacy (OFL) is a strong positive predictor of cryptocurrency investment, whereas subjective financial literacy (SFL) exhibits no significant effect. Demographic differences are evident, with males demonstrating a higher propensity to invest, while investment participation declines with increasing age. Previous investment experience adds limited explanatory power once financial knowledge and demographics are controlled. Within the focused subsample of current, former and intending investors, perceived risk emerges as a significant positive predictor of cryptocurrency participation, unlike trust and environmental awareness. Practical implications As digital-asset regulation evolves, policymakers and platforms should enhance objective financial education, communicate risks clearly, and customize strategies aimed at particular demographic groups. Originality/value By operationalizing SCT's triadic reciprocal determinism framework, this study highlights the distinct roles of OFL and risk perception in cryptocurrency adoption, distinguishing between actual and intended investors and clarifying the relative effects of objective and subjective literacy.
The development of information and communication technology has given rise to various new forms of wealth known as digital assets. These assets include cryptocurrency, monetized social media accounts, digital wallets, websites, internet domains, NFTs (Non-Fungible Tokens), and various other forms of virtual wealth that possess economic value. The presence of digital assets raises new legal issues, particularly in the field of Islamic inheritance. Islamic inheritance law, which has traditionally been oriented toward tangible property, needs to respond to these developments. This study aims to analyze the status of digital assets as inheritance objects from the perspective of Islamic law and to identify various challenges that arise in its implementation. The method used is normative legal research with a conceptual approach and a statute approach. The results indicate that digital assets can be categorized as wealth (māl) that holds economic value and can be inherited as long as their ownership is legitimate according to Sharia. However, several challenges exist, including regulatory limitations, difficulties in asset identification, access to digital accounts, and the absence of a standardized mechanism for digital inheritance distribution. Therefore, the development of Islamic legal ijtihad and the formulation of adaptive regulations are necessary to ensure legal certainty for the heirs.
The purpose of this study is to examine the potential safe-have properties of the two most popular cryptocurrencies, i.e., Bitcoin and Ethereum, against equites, government bonds and gold. To do so, the paper makes use of a daily dataset ranging from 2018 to 2022 acknowledging both the COVID-19 and the potential halving effect in the cryptocurrency market. To robustly assess the research question, the paper employs a quantile GARCH model with non-parametric diagnostics, dynamic Local Projections and rolling window estimations for robustness. The findings of the paper suggest that both assets act as diversifiers against equities and against each other, whereas the halving effect is statistically insignificant and the COVID-19 effect is statistically significantly positive only for the returns of Ethereum. The results imply that cryptocurrencies could contribute to portfolio diversification under stress market conditions.
Sunshine trading theory predicts that publicly disclosing trading intentions can reduce adverse selection and attract liquidity provision, lowering execution costs. Evidence is scarce, because explicit preannouncement of large orders is rare in traditional markets. We study Hyperliquid, a fully on-chain limit order book for cryptocurrency perpetual futures, where protocol-native TWAP orders disclose their terms from inception and remain visible while active, a natural form of sunshine trading. Using address-level data, we reconstruct 4.3 million hidden metaorders and compare them with 465,000 visible TWAP executions. The two execution styles differ sharply: hidden metaorders follow front-loaded, U-shaped schedules consistent with transient-impact optimal execution, whereas TWAPs trade nearly uniformly. We test the preannouncement predictions of Admati and Pfleiderer (1991). Visible TWAPs face lower execution costs than comparable hidden metaorders and leave a smaller permanent price impact. Hidden metaorders executed alongside already-visible same-direction TWAP flow incur higher permanent costs: adverse-selection costs shift toward non-announcers. Finally, visible TWAP programs elicit liquidity provision: while active, displayed depth rises and the book tilts toward the absorbing side, the more so the larger the announced order.
Background: Lyapunov exponent has been used in many science and engineering problems to quantify chaos in systems and understand their nonlinear dynamics. In financial engineering and forecasting, evaluation of chaos in financial data helps determine whether the data are predictable and if profits can be generated. The purpose of this study is to examine presence of chaos in cryptocurrency markets. Methods: To examine chaos, Lyapunov exponent is computed from a set of 50 cryptocurrencies and statistical one-sided and two-sided Student-t tests are performed to check if on average the computed Lyapunov exponents are equal, less, or larger than zero. Results: The statistical results reveal strong evidence that prices, returns, and trading volume changes are all chaotic; hence, they show nonlinear and deterministic characteristics. Conclusions: Prices, returns, and trading volume changes in cryptocurrencies could be predicted in the short run; for instance, on a daily basis. In this regard, active traders and investors may implement predictive systems to generate daily profits.
The rapid proliferation of blockchain technology has fundamentally transformed global finance through the introduction of decentralized digital assets. However, the intrinsic characteristics that define cryptocurrencies namely decentralization, pseudonymity, and transactional irreversibility have simultaneously rendered the ecosystem a primary target for sophisticated cyber-attacks. This study investigates the critical dichotomy between the "code is law" philosophy and the imperative need for robust cybersecurity frameworks within a rapidly expanding market capitalization. This paper provides a multi-layered architectural analysis of vulnerabilities across the network, infrastructure, and application layers of the cryptocurrency ecosystem. Specifically, it examines systemic threats such as 51% attacks, smart contract exploits (including reentrancy and logic bugs), decentralized finance (DeFi) rug pulls, and sophisticated social engineering schemes. To address these vulnerabilities, the study evaluates the efficacy of current defense-in-depth mechanisms, including air-gapped cold storage solutions, multi-signature protocols, third-party smart contract auditing, and privacy-enhancing Zero-Knowledge Proofs (ZKPs). Furthermore, the research explores the integration of regulatory frameworks (AML/KYC standards) and proactive technological defenses like real-time on-chain analytics. Ultimately, this study proposes an enhanced, holistic threat prevention strategy designed to mitigate systemic risks, eliminate single points of failure, and safeguard the future integrity of digital asset platforms.
With the accelerated marketization of data factors, achieving fair contribution evaluation, privacy-preserving verification, and dynamic incentives in decentralized environments has emerged as a critical challenge. Existing studies exhibit a structural tension between privacy protection and verification transparency, while lacking adaptive mechanisms for non-independent and identically distributed (Non-IID) data scenarios. To address these issues, this paper proposes a collaborative trading framework integrating zero-knowledge proofs, personalized federated learning, and reinforcement learning. The framework employs zk-SNARKs to construct non-interactive proofs, thereby resolving the verification-privacy dilemma. A meta-learning–driven personalized aggregation scheme is introduced to correct valuation bias under Non-IID data distributions, and a deep Q-network (DQN) agent is deployed to enable dynamic incentive responses to market supply–demand fluctuations. Experiments conducted on Ethereum and Farcaster datasets demonstrate that the proposed mechanism improves the Contribution Fairness Index (CFI) by 19.7%–22.4% over the strongest baseline, achieving a Verification-Utility Ratio (VER) of 24.6. Under a collaboration scale of N = 20, market vitality entropy increases to 0.75 (baseline: 0.41), effectively suppressing monopolistic tendencies. Moreover, despite the introduction of proof mechanisms, the estimated additional on-chain verification and consensus latency per round is approximately 13 s, calibrated against empirical benchmarks. This work provides a verifiable trading mechanism for data factor markets that jointly ensures privacy, fairness, and efficiency, supporting secure data circulation in domains such as healthcare and finance.
Blockchain-based trading venues, so-called decentralized exchanges, are at the heart of the decentralized finance revolution. Automated market makers, simple computer programs on the blockchain, administer liquidity and set the terms of trade. This article summarizes the key mechanisms behind these new markets, how they differ from traditional financial markets, how liquidity is provided, how prices are set, and how liquidity providers get compensated. We include a short guide on how to understand blockchain data and use these data for academic research.
Purpose This study investigates the volatility spillover dynamics between carbon credit market represented by European Union Allowance (EUA) futures and major cryptocurrencies, Bitcoin (BTC) and Ethereum (ETH), during the 2020–2024 period. It aims to understand whether these assets, despite their difference in regulatory and structural features, exhibit interconnected volatility pattern and particularly under crisis or shock conditions. Design/methodology/approach The article employs a two-step econometric approach. First, the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model is used to estimate time-varying return correlations among EUA, BTC and ETH. And second, the Diebold–Yilmaz (2012) spillovers index based on forecast error variance decomposition is applied to quantify the sizes, directions and evolution of volatility spillovers across markets. Findings The results reveal significant but uneven and time-varying volatility spillovers between carbon and cryptocurrency markets. Spillover intensity becomes more prominent, especially during major crisis periods such as the COVID-19 pandemic, the Russia–Ukraine war and the FTX collapse. Spillovers are asymmetric and regime-dependent. ETH emerges as the main net volatility transmitter, while BTC exhibits a near-neutral and regime-dependent role, alternating between transmitting and receiving shocks. EUA futures remain largely insulated, with only limited outward volatility transmission even under extreme market conditions. These findings suggest the presence of conditional and crisis-driven spillover linkages between green and digital assets. Originality/value This is among the first studies to empirically examine the volatility transmissions between carbon credit and cryptocurrency market using advanced econometric tools. It contributes to the emerging green -digital finance literature by identifying dynamic and directional interdependency across these evolving asset types.
SAMUEL OBOH, Boniface Dondo, S. Yakura Bassa, Gambo I. Bature
Ethereum, a leading digital asset by market value, has gained increasing attention from investors and researchers because of its high price volatility and market unpredictability. This study forecasts Ethereum cryptocurrency daily closing prices using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology, drawing on data from January 1, 2019, to December 31, 2025. Stationarity analysis via the Augmented Dickey-Fuller ADF and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests confirmed that first differencing was required to render the series suitable for the modeling. Through systematic model identification, estimation, and comparison of ten candidate ARIMA specifications, the ARIMA(1,1,0) model emerged as the optimal fit, yielding the lowest information criterion values of Akaike information criterion, Bayesian information criterion (AIC = 24,943.883; AICc = 24,943.84; BIC = 24,955.12). Residual diagnostic tests, including the Ljung-Box test for serial correlation, the Autoregressive Conditional Heteroskedasticity (ARCH-LM) test for heteroscedasticity, and the Shapiro-Wilk test for normality, confirmed that the model residuals are free of serial dependence, although they exhibit time-varying volatility and non-normal distribution, features commonly associated with financial time series. The fitted model was subsequently applied to generate 30-day ahead forecasts with 95% confidence intervals, revealing relatively stable price expectations in the near term alongside progressively widening prediction bands that reflect growing uncertainty over longer horizons. These findings underscore the practical utility of the parsimonious ARIMA(1,1,0) model as a transparent and accessible tool for short-term Ethereum-price forecasting and investment risk assessment.
Yuanyuan Zhang, N. J. Lord, Stephen Chan, Jeffrey Chu · 5 authors
This study examines the relationship between global phishing crime and cryptocurrency-market conditions, with a specific focus on Ethereum. Using monthly data from January 2016 to December 2022, we analyse the returns of global phishing crime numbers together with six Ethereum financial metrics relating to transactions, trading volume, and price impact. We employ quantile regression, quantile-on-quantile regression, and Granger causality in quantiles to examine whether the relationship between Ethereum market indicators and phishing activity varies across different market states. The results reveal a state-dependent relationship. Large increases in phishing crime numbers are strongly associated with large increases in Ethereum transaction activity, average transaction price, and transaction quantity, while implicit transaction cost is predominantly negatively associated with phishing activity, particularly at the upper quantiles. These findings suggest that phishing risk is most pronounced during extreme market conditions and may be shaped by both reward-enhancing market activity and cost-enhancing transaction frictions. To interpret these patterns, we develop an incentive-based criminogenic mechanism in which Ethereum market conditions affect phishing activity through offenders’ expected payoff. We identify two mediating channels: a monetisation-frictions channel, operating through liquidity, price impact, slippage, and transaction costs; and an attention/information-asymmetry channel, operating through volatility, speculative attention, fear of missing out, and user vulnerability. The findings provide initial evidence that cryptocurrency-related phishing is not only a technical cybersecurity issue, but also a market-sensitive phenomenon shaped by financial incentives, liquidity conditions, and behavioural vulnerability. These insights can support regulators, law enforcement agencies, and cryptocurrency platforms in developing adaptive early-warning and prevention strategies.
Quantum computing poses a real, broad-based, but bounded and substantially mitigable threat to Bitcoin and Ethereum. We separate the two quantum algorithms that public discussion routinely conflates: Shor's algorithm breaks the elliptic-curve signatures (ECDSA over secp256k1, BLS over BLS12-381) that authorize spending, whereas Grover's algorithm does not meaningfully threaten proof-of-work mining, which is protected by a merely quadratic speedup, fault-tolerant per-operation costs, a square-root parallelization wall, and difficulty adjustment. Folding hardware scaling, the falling resource requirement, a fault-tolerance readiness lag, and expert surveys into a single Monte-Carlo forecast yields a wide, bimodal arrival distribution for a cryptographically relevant quantum computer: about a one-in-six chance by 2035, near 30% by 2040, and about 60% by 2050. Exposure is concentrated and mostly migratable: of Bitcoin's roughly six million quantum-exposed coins only about 2.3 million are irreducibly at risk, while 50 to 65% of Ether sits at key-revealed accounts that can adopt post-quantum signatures. A timely migration beats even an optimistic 2035 machine, so the binding constraint is governance, not technology. A survey of the top twenty cryptocurrencies finds none fully post-quantum. Reproducible models accompany every quantitative claim.
The paper aimed to investigate the statistical relationship between Bitcoin prices and Ethereum trading volumes, as well as to create a simple predictive model for Ethereum trading volumes based on Bitcoin prices. To perform Spearman’s rank correlation analysis and to construct an artificial neural network (ANN) model, daily closing prices of Bitcoin in USD and daily trading volumes of Ethereum were utilized. The timeframe covered by the data starts May 1, 2020 and ends November 22, 2025. In this study, Ethereum volumes were treated as the dependent variable, while Bitcoin prices served as the independent variable. The findings indicate a significant, moderate, positive correlation between Bitcoin prices and Ethereum volumes, and the ANN model successfully predicted Ethereum volumes with a high level of accuracy. These results reinforce existing evidence regarding the relationships among cryptocurrencies. Furthermore, by confirming the efficacy of artificial neural networks (ANN) in predicting trends within the cryptocurrency market, the study also makes a methodological contribution. In addition, the study also offers a simpler modelling approach that highlights the significance of bilateral interactions among major cryptocurrencies through a single-input model. Based on the impressive performance of the ANN model, exchanges, fintech companies, and investment firms could incorporate lightweight machine-learning systems into their forecasting tools to provide real-time analytics with minimal processing requirements.
This chapter explores the critical role of data polishing and anomaly detection in enabling decentralized finance (DeFi)-driven digital transformation within the energy and utilities industry, with broader implications for dataintensive environments such as cryptocurrency markets and metaverse ecosystems. In an ideal digital infrastructure, decision-making systems operate on transparent, consistent, and high-quality data that support reliable automation, decentralized governance, and predictive analytics. Such an ecosystem presumes seamless data integrity, adaptive risk monitoring, and trustworthy financial and operational exchanges. In practice, however, industrial and financial platforms remain vulnerable to noisy datasets, measurement errors, systemic inconsistencies, and undetected anomalies, which undermine analytical accuracy and institutional confidence. Prior studies on machine learning, data cleaning, and blockchain-based energy systems emphasize preprocessing, normalization, and outlier detection as 78 prerequisites for intelligent operations. Parallel research on crypto-market anomalies and metaverse security highlights the relevance of statistical and learning-based surveillance models. Yet, these strands often remain methodologically fragmented, rarely examining their integrated function within DeFi-enabled infrastructures. This chapter addresses this gap by advancing a unified analytical framework grounded in data reliability theory and decentralized analytics. Focusing on the comparative evaluation of IQR, MAD, and LOF models applied to XRP/USD datasets since 2018, the paper assesses robustness, sensitivity, and computational efficiency. The findings demonstrate how systematic data polishing strengthens trustless financial architectures, enhances operational resilience, and supports sustainable digital transformation in energy and utility ecosystems.
In the contemporary landscape of modernity, characterised by the evolving information age, cryptocurrencies have emerged as a decentralised mode of transaction, qualifying to be termed as liquid modernity (Bauman, 2012). The apparent fluidity, flexibility and the unrevealed potentially rigid tendencies inherent in cryptocurrencies; present it as a virgin domain to be researched with sociological perspectives. This paper aims to understand and outline the history of monetary systems starting from the ancient practice of barter to the establishment of national currencies, and up to the recent advent of cryptocurrency, in an evolutionary framework. As the second objective, this paper attempts to delineate the mechanism of construction and the causal explanations for the adoption and diffusion of cryptocurrency from a sociological lens. In view of the factual status of its legitimation and denial by different governing authorities, the third objective of this paper is to explore into the nuances pertaining to trust, governance and dynamics of power relations with a exploratory concern for rigidity within the claimed fluidity of the cryptocurrency and its utilisation. However, we are assuming one conclusion for our study and that is we are going to get stuck with more significant questions rather than the answers for our objectives.
Kabilesh C M, Dr. B. Raja, Dr. S. Geetha, Dr. V. Cyrilraj
As decentralized finance (DeFi) continues to scale, traditional forensic methodologies often fail due to their retrospective, "post-mortem" nature, analyzing illicit activities only after they are permanently recorded on the ledger. This project proposes coinEth, a real-time institutional blockchain surveillance and autonomous defense system designed for the Ethereum Sepolia network. The framework operates across a four-layer architecture: a Data Acquisition Layer that intercepts pending transactions via Alchemy WebSockets (WSS); a Persistence and Forensic Engine that utilizes SQLite and Python-based heuristics to detect suspicious behavioral patterns such as "structuring" and "high velocity"; a Governance Layer that executes an autonomous enforcement loop via a Solidity-based "Gatekeeper" smart contract; and a Visualization Layer built with Streamlit and PyVis. By assigning dynamic risk scores—categorized as Safe (Level 0), Warning (Level 1), and Frozen (Level 2)—the system can automatically broadcast on-chain transactions to freeze illicit accounts before fund exfiltration occurs. Furthermore, coinEth reconstructs a chronological "money trail" through sequential path mapping (T0 → T1 → T2...), ensuring a verifiable digital chain of custody for investigative reporting. This proactive approach shifts blockchain security from passive observation to active, real-time intervention, significantly enhancing the defense mechanisms available to institutional stakeholders.
Blockchain technology solves central Internet of Things (IoT) forensics challenges—data volatility, device heterogeneity, and chain-of-custody integrity—via decentralized immutability, cryptographically secure systems, and smart contract automaton. Standard forensic software and hardware are not successful in distributed IoT contexts due to centralized reliance and evidence tampering vulnerabilities. Blockchain deployment uses layered architectures (device/edge/blockchain layers) where edge nodes process data in advance and on-chain hashes (e.g., using SHA-256) lock forensic logs onto immutable blockchains. Smart contracts make automation of evidence gathering and custody management tracking, and off-chain storage (e.g., InterPlanetary File System (IPFS)) support big data. Public blockchains like Ethereum and permissioned blockchains like Hyperledger Fabric support contextualized deployments, as seen in smart home, healthcare, and industrial IoT applications. Age-old problems are settling blockchain immutability with General Data Protection Regulation (GDPR) “right to erasure,” scalability for massive-volumes of IoT data with sharding/Layer 2 solutions, and zero-knowledge proofs for upholding privacy. New trends are artificial intelligence (AI)-based anomaly detection, quantum-resistant cryptography, and forensic admissibility standard frameworks (NIST/IEEE).
Blockchain technology has emerged as one of the most transformative innovations of the digital economy, extending far beyond cryptocurrencies into sectors such as finance, healthcare, logistics, governance, and intelligent automation. This study critically examines the role of blockchain technology in enhancing global economic growth through decentralization, transparency, cybersecurity, smart contracts, and digital trust mechanisms. Drawing upon contemporary literature and emerging industrial applications, the paper explores how blockchain contributes to economic resilience, operational efficiency, supply chain optimization, decentralized finance (DeFi), central bank digital currencies (CBDCs), and AI-integrated digital ecosystems. The study adopts a conceptual and analytical approach to evaluate blockchain’s macroeconomic implications and institutional challenges in the context of Industry 4.0. Findings suggest that blockchain has the potential to reduce transaction costs, enhance cross-border economic integration, improve governance transparency, and facilitate sustainable digital transformation. However, regulatory uncertainty, scalability limitations, cybersecurity concerns, and energy consumption remain significant barriers to global adoption. The paper contributes to the literature by proposing an integrated framework linking blockchain innovation with economic sustainability, digital governance, and technological resilience. Policy implications and future research directions are also discussed.