Abstract With the introduction of spot Ethereum ETFs, Ethereum plays an increasingly important role in the cryptocurrency market. In this paper, we propose a Bayesian modelling framework incorporating a mixture copula for co-modelling Ethereum returns with Bitcoin or FTSE 100 returns. The mixture copula is designed as a combination of the Clayton copula and its three rotations, Frank, and Gaussian copulas. It provides substantial flexibility for handling a variety of dependency structures. The Bayesian approach offers the advantage of jointly estimating both the margins and copulas and simulating future returns in a coherent procedure. Using 10 different risk or risk-return measures, we provide updated empirical evidence on Ethereum’s role in both cryptocurrency and mixed portfolios. The analysis not only evaluates its diversification potential numerically but also sheds light on how the optimal allocations vary across distinct risk preferences and portfolio objectives. Moreover, based on the data of 2017–2024, we estimate that Ethereum futures has a hedging effectiveness on Bitcoin of about 30–40% across different risk preferences. Beyond these findings, the Bayesian mixture copula framework represents a methodological contribution to the modelling of complex dependence structures between financial returns. Taken together, our study delivers new insights that are particularly relevant in light of the evolving cryptocurrency landscape and the increasing integration of digital assets into mainstream investment practice.
Kwestan Ahmed Ismael, Heshu Othman Faqe, Mohammed Hussein Abdalla, Hindreen A. Taher
In this work we use historical market data from Bitget to predict weekly open prices of Ethereum (ETH) for a 96-week period with the Prophet forecast model trained by using Particle Swarm Optimization (PSO) algorithm. Because of this, the research delves into automated hyperparameter tuning for Prophet in order to improve forecast performance on cryptocurrency markets where volatility, structural breaks and irregular trading patterns pose a significant challenge to time series prediction. The PSO algorithm is a good method to explore the high dimensional parameter space in which it can strike between the global analysis and local exploitation for detecting minimal forecast errors. Based on evaluating model performance for which we used accuracy metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in training, test holdout & full-fit situations. PSO-optimized Prophet: The results show a great in-sample fitting and fast convergence behaviour, as the best CV RMSE is slightly higher than the lowest one should have obtained if used only 10 iterations. Although forecasts exhibit stability and track long-term trends well, the model does not predict short-term fluctuations in the holdout set with high accuracy (wider forecast uncertainty intervals). Our results shed light on the utility of PSO to improve Prophet-based price prediction in cryptocurrencies, reinforce the relevance of uncertainty quantification in asset markets and inform risk-aware decisions of financial agents dealing with unstable assets.
While the literature features a number of proposals to defend against transaction manipulation attacks, existing proposals are still not integrated within large blockchains, such as Bitcoin, Ethereum, and Cardano. Instead, the user community opted to rely on more practical but ad-hoc solutions (such as Mempool.space) that aim at detecting censorship and transaction displacement attacks by auditing discrepancies in the mempools of so-called observers. In this paper, we precisely analyze, for the first time, the interplay between mempool auditing and the ability to detect censorship and transaction displacement attacks by malicious miners in Bitcoin and Ethereum. Our analysis shows that mempool auditing can result in mis-accusations against miners with a probability larger than 25% in some settings. On a positive note, however, we show that mempool auditing schemes can successfully audit the execution of any two transactions (with an overwhelming probability of 99.9%) if they are consistently received by all observers and sent at least 30 seconds apart from each other. As a direct consequence, our findings show, for the first time, that batch-order fair-ordering schemes can offer only strong fairness guarantees for a limited subset of transactions in real-world deployments.
Abstract Automatic trading systems cope with the needs of put out emotional biases from the trading operation of public assets. These systems place orders based on a price model that forecasts the future price of an asset. Those systems, developed by edge funds and institutional investors, are not available to the public, and extensive research in this field is worth the effort. In this research, we developed a short-term price model based on a neural network and used it to forecast the near-future price direction. More in depth, we introduced the feature extraction process and parametric labeling strategy to build an ML ready dataset that includes more than 400 cryptocurrencies. The model is then validated by building a trading strategy on the two most capitalized cryptos at the time of writing: Bitcoin and Ethereum. The validation uses a trading simulation that spans six years of historical data for Bitcoin and Ethereum, including both retrospective (backtest) and prospective (forward test) evaluations. The results demonstrate that the neural network-based model exhibits a very good generalization to patterns found in historical data, enabling predictions in future data within the trading simulation. In addition, a comprehensive analysis of the importance of features was conducted to enhance the interpretability and performance of the model. Finally, we test our model in a simulated trading session; it shows that, with a simple buy-only strategy plus a stop loss, the trading system limits the draw dawn during bear markets.
Awid Vaziry, Sandro Rodriguez Garzon, Christoph Wronka, Axel Küpper
Public blockchains provide no native mechanism to verify the legal identity behind a deployed smart contract, which blocks institutional adoption and compliance with EU regulations such as MiCA and AMLR. We present KYC Seal, the first protocol that extends the EU eIDAS trust infrastructure to Ethereum smart contracts by cryptographically binding them to Qualified Electronic Seals issued by Qualified Trust Service Providers (QTSPs). The protocol realizes the full eIDAS trust chain, from the European Commission's List of Trusted Lists through Member-State trusted lists and QTSP-signed X.509 certificates down to the individual smart contract, natively on-chain. An on-chain parser extracts identity fields directly from the QTSP-signed certificate bytes at registration. Both cryptographic verifications, the QTSP issuance signature and the certificate holder's seal signature, are performed once at registration and cached as on-chain state, reducing per-interaction seal verification to a pure state check. A new P-256 elliptic-curve precompile in Ethereum (deployed December 2025) makes these one-time cryptographic steps economical, enabling trustless on-chain verification of eIDAS identities without oracles or runtime intermediaries. A reference implementation, a formal security analysis, and a gas evaluation are the subject of forthcoming work.
We develop a mathematical framework to optimize leveraged staking ("loopy") strategies in Decentralized Finance (DeFi), in which a staked asset is supplied as collateral, the underlying is borrowed and re-staked, and the loop can be repeated across multiple lending markets. Exploiting the fact that DeFi borrow rates are deterministic functions of pool utilization, we reduce the multi-market problem to a convex allocation over market exposures and obtain closed-form solutions under three interest-rate models: linear, kinked, and adaptive (Morpho's AdaptiveCurveIRM). The framework incorporates market-specific leverage limits, utilization-dependent borrowing costs, and transaction fees. Backtests on the Ethereum and Base blockchains using the largest Morpho wstETH/WETH markets (from January 1 to April 1, 2025) show that rebalanced leveraged positions can reach up to 6.2% APY versus 3.1% for unleveraged staking, with strong dependence on position size and rebalancing frequency. Our results provide a mathematical basis for transparent, automated DeFi portfolio optimization.
In blockchain systems operating under the Proof-of-Stake (PoS) consensus mechanism, fairness in transaction processing is essential to preserving decentralization and maintaining user trust. However, with the emergence of Maximal Extractable Value (MEV), concerns about economic centralization and content manipulation have intensified. To address these vulnerabilities, the Ethereum community has introduced Proposer Builder Separation (PBS), which separates block construction from block proposal. Later, enshrined Proposer Builder Separation (ePBS) was also proposed in EIP-7732, which embeds PBS directly into the Ethereum consensus layer. Our work identifies key limitations of ePBS by developing a formal framework that combines mathematical analysis and agent-based simulations to evaluate its auction-based block-building mechanism, with particular emphasis on MEV dynamics. Our results reveal that, although ePBS redistributes responsibilities between builders and proposers, it significantly amplifies profit and content centralization: the Gini coefficient for profits rises from 0.1749 under standard PoS without ePBS to 0.8358 under ePBS. This sharp increase indicates that a small number of efficient builders capture most value via MEV-driven auctions. Moreover, 95.4% of the block value is rewarded to proposers in ePBS, revealing a strong economic bias despite their limited role in block assembly. These findings highlight that ePBS exacerbates incentives for builders to adopt aggressive MEV strategies, suggesting the need for future research into mechanism designs that better balance decentralization, fairness, and MEV mitigation.
Cross-chain bridges constitute the single largest vector of systemic risk in Decentralized Finance (DeFi), accounting for over \$2.8 billion in losses since 2021. The fundamental vulnerability lies in the binary nature of existing bridge security models: a bridge is either fully operational or catastrophically compromised, with no intermediate state to contain partial failures. We present ASAS-BridgeAMM, a bridge-coupled automated market maker that introduces Contained Degradation: a formally specified operational state where the system gracefully degrades functionality in response to adversarial signals. By treating cross-chain message latency as a quantifiable execution risk, the protocol dynamically adjusts collateral haircuts, slippage bounds, and withdrawal limits. Across 18 months of historical replay on Ethereum and two auxiliary chains, ASAS-BridgeAMM reduces worst-case bridge-induced insolvency by 73% relative to baseline mint-and-burn architectures, while preserving 104.5% of transaction volume during stress periods. In rigorous adversarial simulations involving delayed finality, oracle manipulation, and liquidity griefing, the protocol maintains solvency with probability $>0.9999$ and bounds per-epoch bad debt to $<0.2%$ of total collateral. We provide a reference implementation in Solidity and formally prove safety (bounded debt), liveness (settlement completion), and manipulation resistance under a Byzantine relayer model.
Matteo Vaccargiu, Riccardo Lai, Maria Ilaria Lunesu, Andrea Pinna · 5 authors
We study how bots contribute to open-source discussions in the Ethereum ecosystem and whether they influence developers' emotional tone. Our dataset covers 36,875 accounts across ten repositories with 105 validated bots (0.28%). Human participation follows a U-shaped pattern, while bots engage in uniform (pull requests) or late-stage (issues) activity. Bots respond faster than humans in pull requests but play slower maintenance roles in issues. Using a model trained on 27 emotion categories, we find bots are more neutral, yet their interventions are followed by reduced neutrality in human comments, with shifts toward gratitude, admiration, and optimism and away from confusion. These findings indicate that even a small number of bots are associated with changes in both timing and emotional dynamics of developer communication.
The delisting of Binance USD (BUSD) constitutes a major regulatory intervention in the stablecoin market and provides a unique opportunity to examine how targeted regulation affects liquidity allocation, market concentration, and short-run systemic risk in crypto-asset markets. Using daily data for 2023 and a linear and nonlinear Local Projections event-study framework, this paper analyzes the dynamic market responses to the BUSD delisting across major stablecoins and cryptocurrencies. The results show that liquidity displaced from BUSD is reallocated primarily toward USDT and USDC, leading to a measurable increase in stablecoin market concentration, while decentralized and algorithmic stablecoins absorb only a limited share of the shock. At the same time, Bitcoin and Ethereum experience temporary liquidity contractions followed by a relatively rapid recovery, suggesting conditional resilience of core crypto-assets. Overall, the findings document how a regulatory-induced exit of a major stablecoin reshapes short-run market dynamics and concentration patterns, highlighting potential trade-offs between regulatory enforcement and market structure. The paper contributes to the literature by providing the first empirical analysis of the BUSD delisting and by illustrating the usefulness of Local Projections for studying regulatory shocks in cryptocurrency markets.
The digitization of medical records in the healthcare sector demands robust mechanisms to ensure data confidentiality, integrity, and privacy. This paper proposes an innovative multi-factor authentication (MFA) mechanism that leverages smart contracts and blockchain technology to secure the tracking of medical records. The proposed system, named Blockchain Authentication with Zero-Knowledge Proof (BAZKP), provides a tamper-proof environment for storing and accessing records while preserving users’ personally identifiable information (PII). A key novelty of BAZKP lies in storing only the character count structure of passwords rather than the actual credentials, combined with zero-knowledge proofs (ZKP) to verify identity without exposing sensitive data. This hybrid blockchain/ZKP approach addresses limitations of centralized and hardware-based solutions, reducing vulnerabilities while avoiding the cost and usability constraints of dedicated hardware systems. The system was implemented and tested on a private Ethereum testnet, with a proof-of-concept application developed using Solidity, Web3.js, and MetaMask. Performance evaluation over 100 transactions for core operations (registration, login, and password reset) demonstrated practical viability: registration incurred the highest latency (≈4500 ms) and gas consumption (≈120,000 gas), while login and reset operations were more efficient (≈4000 ms/80,000 gas and ≈3500 ms/60,000 gas, respectively). Comparative security analysis against existing MFA methods—including 2FA, hardware tokens, and biometrics—confirms that BAZKP provides superior privacy protection through decentralization and ZKP, without the cost and usability drawbacks of hardware-based solutions. Overall, this approach enhances trust in digital health systems by offering a secure, transparent, and privacy-preserving authentication framework for medical data, representing a significant advancement in digital healthcare security. Keywords: Blockchain; Multi-Factor Authentication; Smart Contracts; Zero-Knowledge Proof; Medical Record Security.
Matteo Loporchio, Damiano Di Francesco Maesa, Anna Bernasconi, Laura Ricci
Abstract The ERC-1155 standard introduced on the Ethereum blockchain allows for managing multiple tokens, both fungible and non-fungible, within a single contract. It also supports batch transfers, thereby reducing transaction costs and enabling a more efficient use of blockchain resources. To assess its impact and level of adoption, this paper presents a comprehensive analysis of the ERC-1155 token ecosystem. First, we examine the activity of ERC-1155 contracts and compare the evolution of transfer volumes with those of the two alternative most popular token management standards. Next, we model the economy of each ERC-1155 contract as a directed graph, where nodes represent users and edges denote token transfers. We then study the topological properties of such graphs, analyzing approximately 40,000 networks until the end of 2024. Results indicate that, within our dataset, the adoption of ERC-1155 is growing, although its functionalities are not being fully utilized. Additionally, about 60% of the networks exhibit a completely centralized topology, while the remaining ones are generally sparse and lack small-world characteristics. Finally, the degree distribution analysis shows that preferential attachment is only present in a minority of the networks and the graphs also display a mild disassortative behavior.
Many Ethereum smart contracts rely on block attributes such as block.timestamp or blockhash to generate random numbers for applications like lotteries and games. However, these values are predictable and miner-manipulable, creating the Bad Randomness vulnerability (SWC-120) that has led to real-world exploits. Current detection tools identify only simple patterns and fail to verify whether protective modifiers actually guard vulnerable code. A major obstacle to improving these tools is the lack of large, accurately labeled datasets. This paper presents a benchmark dataset of 1,752 Ethereum smart contracts with validated Bad Randomness vulnerabilities. We developed a five-phase methodology comprising keyword filtering, pattern matching with 58 regular expressions, risk classification, function-level validation, and context analysis. The function-level validation revealed that 49% of contracts initially classified as protected were actually exploitable because modifiers were applied to different functions than those containing vulnerabilities. We classify contracts into four risk levels based on exploitability: HIGH_RISK (no protection), MEDIUM_RISK (miner-exploitable only), LOW_RISK (owner-exploitable only), and SAFE (using Chainlink VRF or commit-reveal). Our dataset is 51 times larger than RNVulDet and the first to provide function-level validation and risk stratification. Evaluation of Slither and Mythril revealed significant detection gaps, as both tools identified none of the vulnerable contracts in our sample, indicating limitations in handling complex randomness patterns. The dataset and validation scripts are publicly available to support future research in smart contract security.
This paper examines the directional connectedness between the returns of Bitcoin and Ethereum and the supply of stablecoins across different market conditions. Using a Quantile Vector Autoregression (QVAR) model, we analyze daily log-returns of major cryptocurrencies and changes in stablecoin supply from January 2021 to November 2024, capturing dynamics at the 5th, 50th, and 95th quantiles. Our findings show that the Total Connectedness Index (TCI) nearly triples under extreme conditions, with Bitcoin and Ethereum transitioning from passive roles in normal periods to dominant transmitters of influence during downturns. Stablecoins behave heterogeneously across regimes, with roles varying significantly even within the same subclass. Tether exhibits state-dependent behavior, acting as a net receiver of shocks in most conditions but emerging as a transmitter during bull markets. We also assessed the impact of the Terra-LUNA collapse, revealing a regime shift in the transmission of shocks: connectedness rises under normal and negative conditions but declines in positive markets. These patterns suggest that, under certain conditions, major cryptocurrencies can influence stablecoin issuance in distinct ways, leading to asymmetric adjustments in supply across individual stablecoins and shaping liquidity dynamics throughout the ecosystem. While we do not attempt to model the underlying mechanisms behind these shifts, our results point to the importance of monitoring state-dependent relationships and recognizing the diverse behaviors of stablecoins. The findings motivate the development of regime-sensitive monitoring tools and support ongoing policy discussions around stablecoin design, issuance frameworks, and market transparency.
ABSTRACT Stablecoins attract academic interest because of their value‐pegging mechanisms and price stability. This likely results in distinct market efficiency. This study compares stablecoins (USDC, Tether, Dai) with Bitcoin and Ethereum and assesses long memory through the Hurst exponent while addressing distortions caused by heavy tails and extreme events. Through shuffled and rank‐order series with a sliding‐window approach, we provide the first reliable time‐varying analysis. The results show that stablecoins exhibit inefficiency and anti‐persistence, with Tether being relatively more efficient. Their tail properties are highly sensitive to extreme events. In contrast, Bitcoin and Ethereum maintain stable weak‐form efficiency even during the COVID‐19 pandemic. These differences are linked to stablecoins' US dollar pegging mechanisms and regulatory constraints. The findings of this study enable comparisons of market efficiency between stablecoins and unpegged cryptocurrencies and offer insights for regulation and investment decisions.
Blockchain technology and cryptocurrencies have attracted significant attention in recent years, yet remain susceptible to cyber threats such as phishing attacks. Existing detection approaches often suffer from high computational costs and limited robustness, especially when facing varying data distributions and sparse structures. To address these issues, we propose Robust, Node behavior, Transaction structure, and Network (R-NTN), a detection framework for Ethereum phishing accounts that leverages multi-dimensional transaction features. R-NTN first constructs 2-hop ego graphs via random walks, then extracts features from three complementary dimensions: behavioral attributes, transaction-based structural features, and network embeddings. These features are integrated into a unified representation for downstream classification. Experiments show that R-NTN consistently outperforms baseline methods and maintains high accuracy across datasets of different scales and compositions, demonstrating strong robustness and generalizability.
The rapid development of Internet of Things (IoT) technology has led to growing concerns about data security and user privacy in the interactions within distributed systems. Decentralized Applications (DApps) in distributed systems consist of on-chain and off-chain functions, where on-chain functions are smart contracts running in the blockchain network, while off-chain functions operate outside the blockchain. Since smart contracts cannot access off-chain information, they cannot verify whether the off-chain functions, i.e. the software components, they interact with have been tampered or not. As a result, establishing mutual trust between the on-chain smart contracts and the off-chain functions remains a significant challenge. To address the challenge, this paper introduces TeeMAF, a generic framework for mutual attestation between on-chain and off-chain functions, leveraging Trusted Execution Environments (TEE), specifically Intel Software Guard Extensions (SGX), SCONE (a TEE container on top of Intel SGX), and remote attestation technologies. This ensures that the deployed off-chain functions of a DApp execute in a provably secure computing environment and achieve mutual attestation with the interacting on-chain functions. Through a security analysis of TeeMAF, the reliability of deployed DApps can be verified, ensuring their correct execution. Furthermore, based on this framework, this paper proposes a decentralized resource orchestration platform (a specific DApp) for deploying applications over untrusted environments. The system is implemented on Ethereum and benchmarked using Hyperledger Caliper. Performance evaluation focusing on throughput and latency demonstrates that, compared to platforms without a mutual attestation scheme, the performance overhead remains within an acceptable range.
Forensische Notizen und Sicherungserklärung Beweishandhabung, Metadatenintegrität und Chain of Custody Geltungsbereich Diese Erklärung dokumentiert die Handhabung, Sicherung und Bewahrung digitaler Beweismittel im Rahmen des forensisch-wissenschaftlichen Gutachtens SIA Security Intelligence Artefact – Technologie, Software und Familien-Historie Aktenzeichen: INT-CODE-2025-BTC/ETH-CORE-ISABELSCHOEPSTHIEL bitte beachten Sie mein HELPME.md Beweishandhabung und Nicht-Veränderungs-Grundsatz Alle relevanten Dateien, einschließlich Rohdaten, Quellmaterialien und dokumentarischer Artefakte, wurden in einen dedizierten Evidence-Ordner überführt. Der interne Dateiinhalt wurde nicht verändert. Es wurden weder Code, Text, Metadaten, Autoreneinträge, Benutzerkennungen, Zeitstempel noch sonstige Provenienzangaben modifiziert. Insbesondere unverändert erhalten blieben: Ursprüngliche Ersteller und Mitwirkende gemäß Metadaten Benutzerkennungen und Autorschaftsspuren Zeitstempel, Hashes und interne Verlaufsdaten Programmiersprache, Workflow-Logik und interne Struktur Die ursprüngliche Herkunft und Urheberschaft jeder Datei ist damit vollständig forensisch auslesbar und beweissicher erhalten. Dateisystem-Sicherungsmaßnahmen Um eine weitere Ausführung, Verbreitung oder operative Nutzung potenziell schädlicher Workflows zu verhindern, wurden ausschließlich externe Ordner- und Dateinamen auf Dateisystemebene angepasst. Diese Maßnahmen beschränkten sich auf: Umbenennung von Ordnern und Top-Level-Dateinamen Deaktivierung von ausführbaren oder workflow-auslösenden Bezeichnungen Der Dateiinhalt, der Code und sämtliche Metadaten blieben unangetastet. Diese Maßnahmen dienten ausschließlich der Gefahrenabwehr bei gleichzeitiger vollständiger Beweissicherung. Ethischer und rechtlicher Kontext Im Rahmen der Sichtung wurden Hinweise auf schwere ethische und rechtliche Verstöße festgestellt, unter anderem: Unbefugte Datenmanipulation Datenmissbrauch und Datendiebstahl Aneignung geistigen Eigentums Invasive Profilierungs- oder Auswertungspraktiken Aus diesem Grund wurde die operative Ausführbarkeit neutralisiert, während die forensische Beweisstruktur vollständig erhalten blieb. Screenshot-basierte Beweissicherung Zur Dokumentation wurden an allen relevanten Stellen Screenshots erstellt und dem Evidence-Ordner beigefügt. Die Screenshots: sind unbearbeitet und unbeschriftet enthalten die ursprüngliche Ordner- und Dateistruktur zeigen die sichtbaren Benutzernamen, Akteure und Eigentümer der jeweiligen Verzeichnisse Dadurch bleiben alle beteiligten Accounts, Strukturen und Verantwortlichkeiten objektiv nachvollziehbar. Forensische Integrität Alle Maßnahmen wurden unter Einhaltung folgender Prinzipien durchgeführt: Keine Kontamination der Originaldaten Keine Veränderung von Metadaten Vollständige Nachvollziehbarkeit für unabhängige Forensik Sicherung der gerichtlichen Verwertbarkeit Alle Materialien sind hash-prüfbar, chain-of-custody-fähig und für externe Gutachten geeignet. Signatur und Verwahrung Unterzeichnet und bestätigt durch: Frau Isabel Schöps, geborene Thiel Cyriakstraße 30c D-99094 Erfurt Thüringen, Deutschland Rolle: Autorin, Rechteinhaberin, Hauptverwahrerin ORCID (Person): 0009-0003-4235-2231 https://orcid.org/0009-0003-4235-2231/print ORCID (Institutionell / Projekt): 0009-0006-8765-3267 https://orcid.org/0009-0006-8765-3267/print Diese Erklärung ist Bestandteil der DOI-archivierten Chain of Custody und dient der rechtlichen, forensischen und menschenrechtlichen Prüfung. Englisch Forensic Notes and Preservation Statement Evidence Handling, Metadata Integrity and Chain of Custody Scope This note documents the handling, preservation, and safeguarding of digital evidence associated with the forensic-scientific work SIA Security Intelligence Artefact – Technology, Software and Family History Case Reference: INT-CODE-2025-BTC/ETH-CORE-ISABELSCHOEPSTHIEL Evidence Handling and Non-Alteration Policy All relevant files, including raw data, source materials, and documentary artefacts, were transferred into a dedicated Evidence directory for preservation and review. No internal file contents were modified. No code, text, metadata, authorship fields, user identifiers, timestamps, or embedded provenance information were altered. Specifically preserved without change: Original creators and contributors as recorded in file metadata User identifiers and authorship traces Timestamps, hashes, and internal history Programming language, workflow logic, and structural dependencies inside the files The original provenance and authorship of each file therefore remain fully readable and forensically extractable. File System Safety Measures To prevent any further unintended execution, propagation, or operational misuse of potentially harmful workflows, only external file and folder names were adjusted at the file-system level. These actions were limited to: Renaming folders and top-level file names Disabling executable or workflow-triggering identifiers No internal data, code, or metadata were altered. These measures were implemented solely to prevent further operational impact while preserving evidentiary value. Ethical and Legal Context During review, multiple files indicated serious ethical and legal concerns, including but not limited to: Unauthorized manipulation of data Data misuse and data theft Misappropriation of intellectual property Invasive profiling or exploitative data practices For this reason, operational execution was neutralized while forensic preservation was strictly maintained. Screenshot-Based Evidence Capture For evidentiary verification, screenshots were taken at each relevant stage and stored within the Evidence directory. The screenshots: Remain unedited and unlabelled Preserve original folder structures and visual context Display usernames, account identifiers, and responsible actors visible at the time of capture This ensures that all observed actors, file ownerships, and directory relationships remain objectively documented and reviewable. Forensic Integrity All actions taken were designed to satisfy the following principles: No contamination of original data No destruction or modification of metadata Full traceability for independent forensic analysis Preservation of evidentiary admissibility All materials are suitable for hash verification, chain-of-custody tracking, and independent expert review. Signature and Custodianship Signed and certified by: Frau Isabel Schöps, née Thiel Cyriakstraße 30c D-99094 Erfurt Thuringia, Germany Role: Author, Rights Holder, Principal Custodian ORCID (Individual): 0009-0003-4235-2231 https://orcid.org/0009-0003-4235-2231/print ORCID (Institutional / Project): 0009-0006-8765-3267 https://orcid.org/0009-0006-8765-3267/print This statement forms part of the DOI-archived Chain of Custody and is intended for legal, forensic, and human-rights review. Meine Referenz Datenbank, verknüpft mit meinem aktuellen GitHub-Account* Meine Ersuchen an die Vereinten Nationen - Bitte helfen Sie mir Schöps geb. Thiel, I. (2025). Meine Ersuchen an die Vereinten Nationen - Bitte helfen Sie mir (Zenodo.org). Zenodo.org, University of Harvard harvard.edu, Oxford University ox.ac.uk, Cambridge UK, Reuters.com, New York Times nyt.com, Springer Nature Springer.com, GitHub github.com, University Arizona, Vereine Nationen UN unric.org,. https://doi.org/10.5281/zenodo.18025762 Zenodo-Datenbank und Chain of Custody Volumen 4 Schöps (Thiel), I., Schöps (Thiel), I. und Schöps geb. Thiel, I. (2025) "Yellow White Paper – Bitcoin & Ethereum", Yellow White Paper – Bitcoin & Ethereum. 1st Aufl. D-99094 Erfurt, Thueringa, Germany: Harvard University, University Cambridge, University of Oxford, Springer Nature, Zenodo, S. 109 pages. doi:10.5281/zenodo.17807324. Volumen 3 Schöps geb. Thiel, I. (2025) SIA Security Intelligence Artefact – Volume 3 - Familiäre Erblinie deutschen Monarchie und letzten Kaiserreich. 1st Aufl, The Decline and Fall of the Habsburg Empire, 1815-1918. 1st Aufl. Zenodo, University Harvard Cambridge Press, Oxford University Press Lizenz-ID 6131130060979, Springer Verlag. doi:10.5281/zenodo.18013057. Volumen 2 Schöps geb. Thiel, I. (2025) "Volumen 2 - SIA-Security-ntelligence-Artefact-Chain-of-Custody-Forensische-Familien-Monarchielinie-copyright-isabelschoepsthiel-urheberin-autorin-.docx.pd", Trillion Dollar Bitcoin. 1st Aufl. D-99094 Erfurt, Germany, Thüringen: Zenodo, University Harvard Cambridge Press, Oxford University Press Lizenz-ID 6131130060979, Springer Verlag. doi:10.5281/zenodo.17852789. Volumen 1 Schöps geb. Thiel, I. (2025) "Volumen 1 - SIA Security Intelligence Artefact by Isabel Schoeps geb. Thiel", Trillion Dollar Bitcoin. 1st Aufl. D-99094 Erfurt, Germany, Thüringen: Zenodo, University Harvard Cambridge Press, Oxford University Press Lizenz-ID 6131130060979, Springer Verlag. doi:10.5281/zenodo.17809724. My Developer Signatur Signed-on-by: Frau Isabel Schöps, geborene Thiel Autorin, Urheberin und Auftraggeberin Rechtscharakter: Eidesstattliche Versicherung, Bestandteil des forensisch, wissenschaftlichen Gutachtens Titel: SIA Security Intelligence Artefact internationinternationale Kennung: INT-CODE-2025-BTC/ETH-CORE-ISABELSCHOEPSTHIEL OrcID: 0009-0003-4235-2231 Isabel Schöps Thiel OrcID: 0009-0006-8765-3267 SI-IST Isabel Schöps Aktueller Wohnort und Meldeanschrift: Cyriakstrasse 30c, D-99094 Erfurt, Thüringen, Deutschland, gemeinsam mit meinen vierbeinigen Freund, American XL-Bully Don Offizielle institutionelle Würdigung, Danksagung - Präfix_Referenz: YWP-1-IST-SIA YWP-1-5-IST-SIA Pseudonyme und Alias: Satoshi Nakamoto, Vitalik Buterin, GitHub, Octocat, Johnny Appleseed, IST-GitHub, Cristina_Bella, Nick Szabo, John Appleseesd Offizielles weltweit erstes Developer Certifikat: Developercertificate <img width="642" he
Smart contract technology facilitates self-executing agreements on the blockchain, eliminating dependency on an external trusted authority. However, smart contracts may expose vulnerabilities that can lead to financial losses and disruptions in decentralized applications. In this work, we evaluate deep learning-based approaches for vulnerability scanning of Ethereum smart contracts. We propose VASCOT, a Vulnerability Analyzer for Smart COntracts using Transformers, which performs sequential analysis of Ethereum Virtual Machine (EVM) bytecode and incorporates a sliding window mechanism to overcome input length constraints. To assess VASCOT's detection efficacy, we construct a dataset of 16,469 verified Ethereum contracts deployed in 2022, and annotate it using trace analysis with concrete validation to mitigate false positives. VASCOT's performance is then compared against a state-of-the-art LSTM-based vulnerability detection model on both our dataset and an older public dataset. Our findings highlight the strengths and limitations of each model, providing insights into their detection capabilities and generalizability.
A sharing framework based on Zero-Knowledge Proof (ZKP) and Proxy Re-encryption (PRE) technologies offers a promising solution for sharing Student Electronic Academic Records (SEARs). As core credentials in the education sector, student records are characterized by strong identity binding, the need for long-term retention, frequent cross-institutional verification, and sensitive information. Compared with electronic health records and government archives, they face more complex security, privacy protection, and storage scalability challenges during sharing. These records not only contain sensitive data such as personal identity and academic performance but also serve as crucial evidence in key scenarios such as further education, employment, and professional title evaluation. Leakage or tampering could have irreversible impacts on a student’s career development. Furthermore, traditional blockchain technology faces storage capacity limitations when storing massive academic records, and existing general electronic record sharing solutions struggle to meet the high-frequency verification demands of educational authorities, universities, and employers for academic data. This study proposes a dedicated sharing framework for students’ electronic academic records, leveraging PRE technology and the distributed ledger characteristics of blockchain to ensure transparency and immutability during sharing. By integrating the InterPlanetary File System (IPFS) with Ethereum Smart Contract (SC), it addresses blockchain storage bottlenecks, enabling secure storage and efficient sharing of academic records. Relying on optimized ZKP technology, it supports verifying the authenticity and integrity of records without revealing sensitive content. Furthermore, the introduction of gate circuit merging, constant folding techniques, Field-Programmable Gate Array (FPGA) hardware acceleration, and the efficient Bulletproofs algorithm alleviates the high computational complexity of ZKP, significantly reducing proof generation time. The experimental results demonstrate that the framework, while ensuring strong privacy protection, can meet the cross-scenario sharing needs of student records and significantly improve sharing efficiency and security. Therefore, this method exhibits superior security and performance in privacy-preserving scenarios. This framework can be applied to scenarios such as cross-institutional academic certification, employer background checks, and long-term management of academic records by educational authorities, providing secure and efficient technical support for the sharing of electronic academic credentials in the digital education ecosystem.
Chimeremma Sandra Amadi, Simeon Okechukwu Ajakwe, Taesoo Jun
Secure and auditable data sharing in large-scale Internet of Things (IoT) environments remains a significant challenge due to weak trust coordination, limited scalability, and susceptibility to emerging quantum attacks. This study introduces a hybrid blockchain-based framework that integrates post-quantum cryptography with intelligent anomaly detection to ensure end-to-end data integrity and resilience. The proposed system utilizes Hyperledger Fabric for permissioned device lifecycle management and Ethereum for public auditability of encrypted telemetry, thereby providing both private control and transparent verification. Device identities are established using quantum-entropy-seeded credentials and safeguarded with lattice-based encryption to withstand quantum adversaries. A convolutional long short-term memory (CNN–LSTM) model continuously monitors device behavior, facilitating real-time trust scoring and autonomous revocation via smart contract triggers. Experimental results demonstrate 97.4% anomaly detection accuracy and a 0.968 F1-score, supporting up to 1000 transactions per second with cross-chain latency below 6 s. These findings indicate that the proposed architecture delivers scalable, quantum-resilient, and computationally efficient data sharing suitable for mission-critical IoT deployments.
Non-fungible tokens (NFTs) are widely used to distribute and authenticate digital artworks, yet minting practices across Ethereum, Solana, and Tezos sometimes diverge from core blockchain objectives, decentralization, immutability, and verifiable on-chain identity, thereby limiting their suitability as long-term certificates of authenticity. This study examines the technical, structural, and archival sources of these limitations and introduces a framework grounded in immutability, extended metadata and cryptographic signatures. Within this framework, the extended metadata file serves as the primary source of authenticity by self-sufficiently integrating the artist’s identity, artwork identification, edition semantics, token identification, and preservation guidance. The paper further evaluates decentralized storage models relevant to certification-grade use cases. It compares private servers, fully on-chain metadata, Arweave, and IPFS in terms of verifiability, sustainability, and long-horizon stewardship burden. Although Ethereum's ERC-721 serves as a reference implementation to anchor terminology and verification procedures, the proposed requirements are ecosystem-independent as long as the ecosystem meets the principles of decentralization, immutability and adoption. By aligning NFT issuance with established digital-preservation frameworks (OAIS, InterPARES, LOCKSS, PREMIS), this work offers a concrete foundation for future ERC/EIP proposals, preservation policy, and interdisciplinary research on durable, independently verifiable digital-art authenticity.
Blockchain is a decentralized, distributed ledger technology that ensures transparency, security, and immutability through cryptographic techniques. However, advancements in quantum computing threaten the security of classical cryptographic schemes, jeopardizing blockchain integrity once cryptographic quantum supremacy is achieved. This milestone, defined here as the realization of quantum computers to solve practical cryptographic problems, would render existing security standards vulnerable, exposing blockchain assets (currency, data, etc.) to fraud and theft. To address this risk, we propose and implement a smart contract deployable on the Ethereum blockchain, having the ability to run applications on its blockchain, that generates classically intractable puzzles by probabilistically generating large, hard-to-factor numbers without requiring secret information. This contract then serves two purposes: to establish a mechanism (1) for a trustless, unbiased proof of cryptographic quantum supremacy by verifying solutions to these puzzles, and (2) to protect user funds on Ethereum by triggering quantum-secure fallback protocols upon detecting cryptographic quantum supremacy, since it is desirable to wait as long as possible to fall back to a quantum-secure scheme because of its inherent additional cost and complexity. These mechanisms demonstrate the ability to identify cryptographic vulnerabilities and ensure a smooth transition to quantum-secure standards, safeguarding blockchain assets in a post-quantum era.