Sofia Bobadilla, Humaira Afrin, Angela Novelli, Martin Monperrus
Blockchains are among the most adversarial environments in computing. Billions are stolen by cybercriminals who exploit vulnerabilities. This is an open problem and no concept or technique has proven to really make a difference. In this paper, we claim that the classical notion of program invariant is perhaps the most powerful solution to the problem. We devise anoriginal experimental protocol to 1) study how invariants would have protected against past real-world attacks and 2) whether state-of-the-art automated tools can find them. The experimental toolchain is sophisticated. It is based on INVARIANTEVAL, a benchmark of 28 real Ethereum exploits, each paired with a human-authored invariant that blocks the attack. We validate every invariant with PONDEREPLAY, a replay framework that re-executes transactions in order to prove the correctness and soundness of smart contract invariants. We demonstrate that smart contract invariants block all the cybercriminal attacks in INVARIANTEVAL, fully validated by replaying 108,637 historical transactions. Our large-scale experiments clearly demonstrate that smart contract invariants protect against cybercriminals.
Public blockchain data enables large-scale DeFi-related analysis, but many existing approaches are application-specific, difficult to scale, or hard to interpret. This research proposes a scalable, application-agnostic framework for \emph{persistent behavioural pattern discovery} from large-scale blockchain activity. It constructs behaviour sentences enriched with contract, token and market context, then applies a two-step embedding process: sentence-level embeddings capture individual actions, while sequence-level embeddings capture user behaviour over time. An interpretable behavioural profiler characterizes discovered communities through behavioural motifs, routines, temporal dynamics, entity exposure, and suspiciousness evidence. Evaluation on Ethereum using over 30 million transactions shows that the framework uncovers both routine and malicious behavioural patterns, including decentralised exchange (DEX) trading, NFT activity, phishing, bot operations, oracle manipulation, and rug-pull schemes. Importantly, many patterns remain stable across independent observation windows, enabling the identification of long-term behaviours beyond a single analysis period. The proposed framework combines scalability, interpretability, and persistence analysis, supporting blockchain forensic investigation, behavioural attribution, and threat discovery.
This paper presents a verified funder landscape and fit-score shortlist for sustaining QNFO, a two-year-old, solo-run, AI-assisted research platform that has produced an open corpus of approximately 1,000 method papers across seven program areas. Every funder fact was verified by live HTTP retrieval on 2026-08-13 across twenty-six pages spanning Web3 and IPFS ecosystem grantors, open-science philanthropy, and decentralized-science programs; anything not verified live is explicitly flagged. The analysis scores eleven funders on eligibility for an unaffiliated individual, topical fit with decentralized and epistemics-oriented research, and application friction, yielding a weighted ranking led by NLnet NGI Zero (calls open September 3, 2026; deadline November 3, 2026, 12:00 CEST) and Emergent Ventures, followed by the Foresight Institute, Filecoin Foundation, the Ethereum Ecosystem Support Program, Gitcoin, and the Effective Altruism funds. A sequencing calendar spans August 2026 through 2027, including the Sovereign Tech Agency Fellowship cycle. The paper documents application-readiness gaps (legal entity, residency, tax position, public identity), per-funder pitch skeletons, and framing cautions, including the risk of presenting corpus volume as rigor. It closes with an agent-executable action plan.
Although stablecoins occupy a segment of digital-asset markets in which price stability is central by design, their temporary departures from reference values may reveal important information about latent risk and market stress. In this paper, we examine whether bubble and crash signals extracted from traditional cryptocurrencies and stablecoins improve volatility, Value-at-Risk, and Expected Shortfall forecasting and, in connection with these forecasting gains, contribute to the assessment of cross-asset contagions. The analysis applies the Bubble Crash–GARCH models, in which extreme price phases are identified through the Phillips, Shi, and Yu real-time monitoring procedure and incorporated into the conditional mean of returns through event-based dummy variables. For stablecoins, extreme episodes are not inferred from price dynamics in isolation but from deviations between the observed price and the asset-specific reference value. The empirical investigation focuses on Bitcoin, Ethereum, Tether’s USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubble–crash effects and bidirectional contagion channels between traditional cryptocurrencies and stablecoins, using Bitcoin and Tether as the leading representatives of the two market segments. The findings indicate that accounting for bubble and crash episodes leads to more accurate volatility forecasts than standard GARCH benchmarks. For Value-at-Risk and Expected Shortfall, the bubble–crash specifications can improve tail risk forecasting at several tail probability levels through more accurate coverage, lower quantile loss, and stronger ESR backtesting performance. The results also reveal different degrees of price exuberance across the two asset categories: while extreme price dynamics are more evident among traditional cryptocurrencies, deviations from fundamentals are rare for stablecoins. Among stablecoins, USDT exhibits limited but detectable exuberance, whereas Tether Gold does not display extreme price episodes. However, when such deviations occur, as in the case of USDT, they generate significant contagion effects on major cryptocurrencies. Notably, extreme episodes originating in USDT have a stronger impacts on Bitcoin and Ethereum than the reverse spillovers from traditional cryptocurrencies to USDT. Overall, the evidence suggests that stablecoins are not merely passive instruments within the digital-asset ecosystem. Even temporary deviations from their reference values contain valuable information for risk forecasting and contagion monitoring.
Dušan Mitrović, Ivan Milenković, Miroslav Minović
The growing use of blockchain in e-commerce has produced hybrid environments in which private enterprise ledgers and public blockchain networks operate side by side. Consequently, efficient and secure interoperability between these networks has become increasingly important. This study presents a cross-chain interoperability framework that links a permissioned Hyperledger Fabric network with a public Ethereum network. The framework provides attestations of selected business events rather than moving assets. An interoperability smart contract on Fabric emits cross-chain events; an off-chain validator enforces uniqueness and replay protection; and a public verification contract on Ethereum records an immutable, publicly verifiable attestation of each event. The framework uses a two-of-three validator threshold to attest events, so safety holds as long as no more than one of the three validators is compromised. The prototype was evaluated by processing 21,000 events across sequential, concurrent, and peak-load workloads. On the local network, message validation averaged approximately 12 ms per event, and the interoperability layer added less than 200 ms of overhead per attestation. Sustained throughput ranged from 13.2 to 14.2 attestations per second, while the validator used approximately 16% mean CPU and less than 194 MiB of memory, with no sustained memory growth during the full experiment. On the Ethereum Sepolia public testnet, 55 transactions were confirmed with a 100% success rate and a mean confirmation time of 10,676.62 ms. Gas consumption stayed stable at about 51,743 gas per verification on the local network and about 189,092 gas on Sepolia, and the mean public testnet transaction cost was 0.000692 Sepolia ETH. Five adversarial tests were conducted, covering replay, forgery, malicious relayers, concurrent replay, and denial-of-service attacks. All five tests passed, including the rejection of 500 concurrent replay attempts with zero double registrations. The results show that the framework provides efficient, verifiable, and replay-resistant cross-chain interoperability suited to hybrid e-commerce ledgers.
Hebat Allah Adel, sayed abdelgaber, Wessam H. El-Behaidy
Ensuring transparency and security in digital recruitment systems remains a critical challenge. This study proposes BC-XAIA, a unified framework that integrates blockchain, smart contracts, explainable artificial intelligence (XAI), and agile methodology to enable consistent, secure, and traceable recruitment decision-making. Smart contracts, implemented in Solidity and deployed using the Remix Ethereum IDE, automate key processes such as identity verification, data access control, and behavior monitoring, reducing reliance on centralized intermediaries. To support intelligent decision-making, multiple machine learning models, including Random Forest, Logistic Regression, and Support Vector Machine (SVM), were trained and evaluated on a recruitment dataset, with Random Forest achieving the highest performance, reaching an accuracy of 93%. To enhance transparency, SHAP and LIME were employed to provide both global and local interpretability of model predictions. Furthermore, agile methodology is embedded to drive continuous adaptation, iterative development, and stakeholder feedback throughout the recruitment lifecycle. Unlike existing recruitment systems that treat blockchain, AI, and explainability separately, BC-XAIA unifies these technologies within an agile and decentralized architecture. Overall, BC-XAIA establishes a secure, transparent, and explainable decentralized recruitment ecosystem that enhances trust, fairness, and intelligent decision-making in next-generation HR systems.
Transformer-based detectors for Solidity smart contracts almost universally encode a contract within a single 512-token window, then attribute performance differences to the choice of pre-trained encoder. We show this attribution is misplaced. On DIVE-25 (22,330 deployed contracts, eight DASP categories, multi-label at 2.46 labels per contract) the median contract occupies 2,994 sub-word tokens and only 5.48% fit a single window. We segment each contract at top-level declaration boundaries, pack the segments greedily into at most 24 chunks of 510 tokens for an effective context of 12,240 tokens covering 98.25% of the corpus, and recombine the chunk representations with a bidirectional LSTM under additive attention. Holding preprocessing, chunk budget, pooling, aggregator, loss, schedule, seeds and split identical, the extended context is worth +0.1038 micro-F1 and +0.1722 macro-F1 over single-window truncation, roughly four times the benefit of the best available encoder. The loss under truncation is markedly uneven: Front Running falls by 0.294 and Time manipulation by 0.262, while Access Control, whose indicators sit near the top of a file, loses 0.010. Fifty-one structural measurements and a five-relation contract graph enter the classifier through per-class gates initialised at σ(−4) ≈ 0.018, so any contribution must be learned; both open, and the resulting gain is 1.9 times larger on categories below 900 test instances. Under family-aware leakage-controlled partitioning the complete system reaches 0.8435 micro-F1 and0.7775 macro-F1, with the fusion gain significant under a paired bootstrap (macro-F1 +0.0173, 95% CI [+0.0113, +0.0236]). We report every result additionally on a twin-free test subset from which the 39.58% of test contracts sharing a structural twin with training are removed. Finally, evaluated against human-verified exploitability judgements the detector scores 0.455 mean AUC, below a baseline built from contract size and compiler version alone (0.735), bounding what any detector trained on analyser consensus can be claimed to do.
Land ownership management is a critical administrative process that requires secure record maintenance, transparent ownership verification, and efficient property transfer mechanisms. Conventional land registry systems primarily depend on centralized databases and paper-based documentation, making them susceptible to document forgery, unauthorized modifications, duplicate ownership claims, lengthy verification procedures, and administrative inefficiencies. These limitations often result in ownership disputes, reduced public trust, and delays in property transactions. This paper presents a Blockchain-Enabled Secure Land Registry Framework that leverages blockchain technology to establish a decentralized, transparent, and tamper-resistant platform for land registration and ownership management. The proposed system integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firebase Firestore manage user authentication, supporting documents, and application data. Ethereum smart contracts developed using Solidity are employed to securely record land registration, government verification, and ownership transfer transactions on the blockchain, with Ganache serving as the blockchain testing environment. Every approved transaction generates a unique blockchain transaction hash, enabling secure verification, complete traceability, and immutable ownership history. The hybrid architecture combines the scalability of cloud-based data management with the integrity of blockchain technology to ensure efficient record retrieval while preventing unauthorized alterations. The implemented framework demonstrates secure land registration, transparent ownership transfer, simplified government verification, and reliable auditability with minimal operational complexity. The proposed solution provides a scalable and cost-effective approach for modern digital land administration and establishes a strong foundation for future integration with national land registries, electronic identity verification, GIS-based property mapping, and mobile-enabled citizen services.
Secure and transparent attendance management has become increasingly important in educational institutions as conventional attendance systems often face challenges such as proxy attendance, unauthorized record modification, and limited traceability. Most existing solutions rely on centralized databases, making them susceptible to data tampering, accidental loss, and single-point failures. This paper presents a Blockchain-Based Attendance Management System that leverages blockchain technology to provide a decentralized and immutable mechanism for recording and verifying attendance information. The proposed framework integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firestore manage user authentication and application data. Attendance records are securely stored through Ethereum smart contracts executed on the Ganache blockchain network, with transaction hashes linked to Firebase for efficient retrieval and verification. This hybrid architecture combines the scalability of cloud-based data management with the integrity and transparency of blockchain technology. Once attendance is recorded, the information cannot be altered without detection, ensuring reliable auditability and improved trust among students, faculty members, and administrators. The implemented system demonstrates secure attendance recording, fast verification, and efficient transaction management while reducing the possibility of record manipulation. The proposed solution offers a practical, scalable, and cost-effective approach for modern attendance management and provides a strong foundation for future enhancements such as biometric authentication, QR code-based attendance, and cloud-enabled blockchain deployment.
The rapid growth of digital education and online recruitment has significantly increased the demand for reliable academic credential verification. Conventional certificate verification methods are often centralized, time-consuming, and susceptible to document forgery, unauthorized modification, and administrative delays. To address these challenges, this paper presents a Blockchain-Enabled Decentralized Framework for Secure Academic Certificate Issuance and Real-Time Verification. The proposed framework utilizes Ethereum blockchain technology through Solidity smart contracts to establish an immutable and transparent repository of certificate records, ensuring that issued credentials cannot be altered without detection. A SHA-256 cryptographic hashing mechanism is employed to generate unique digital fingerprints for each certificate, while Firebase Authentication and Cloud Firestore provide secure identity management and efficient off-chain metadata storage. The user interface is developed using React.js, enabling educational institutions to issue certificates and allowing employers, universities, and other stakeholders to verify credentials instantly through a simple web-based platform. During verification, the system recomputes the certificate hash and compares it with the blockchain record to detect tampering and validate authenticity in real time. Experimental evaluation on a local Ethereum network demonstrates reliable certificate issuance, rapid verification with sub-second response times, secure transaction handling, and effective resistance against certificate forgery. The proposed framework enhances transparency, trust, and operational efficiency while minimizing manual verification efforts. Furthermore, its modular architecture facilitates future migration to public blockchain networks and decentralized storage platforms, making it suitable for scalable deployment across educational institutions and digital credential ecosystems.
Tokenized representations of cash-like instruments, comprising stablecoins, tokenized money market funds, and tokenized real-world assets, are increasingly positioned as core on-chain financial infrastructure, yet empirical evidence on how these instruments behave in practice remains limited. This paper reports a comparative empirical examination of public transaction-level blockchain data, covering adoption patterns, usage dynamics, and operational characteristics across three parallel case studies: USDC (stablecoin, Circle), BENJI (tokenized money market fund, Franklin Templeton), and BUIDL (tokenized U.S. Treasury, BlackRock via Securitize). On-chain metrics covering issuance and redemption activity, transfer behavior, wallet concentration, velocity proxies, and cross-chain deployment are interpreted against a four-layer reference architecture (asset representation, control-plane governance, settlement and finality, and composability). Results reveal systematic behavioral differences aligned with product intent and governance design: stablecoins function as high-velocity settlement instruments with broad address distribution, while tokenized investment products exhibit batch-oriented issuance, low circulation intensity, and concentrated holdings consistent with institutional custody and regulatory constraints. A live-pipeline extraction for BUIDL on Ethereum over the 90-day window ending 31 January 2026 yields a holder-level Gini coefficient of 0.8706 with a bootstrap 95% confidence interval of [0.7672, 0.9208] and a top-ten concentration share of 98.96%. Cross-chain deployment expands access but preserves reliance on dominant settlement layers. These patterns constitute an evidence-based framework for evaluating tokenized finance as production-grade financial market infrastructure.
The rapid growth of AI-driven workloads, IoT ecosystems, and distributed digital services has exposed fundamental limitations in existing cloud and edge infrastructures, particularly in guaranteeing robust data privacy under emerging quantum threats. Current blockchain-based systems provide integrity and decentralization but rely predominantly on computational cryptography and access-control mechanisms, leaving them vulnerable to future quantum adversaries and large-scale inference attacks. In this paper, we introduce Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework. Our approach integrates three complementary privacy protection layers: (i) MuReQua, a quantum-resilient blockchain consensus mechanism leveraging CQKD for cryptographic robustness against Shor-type attacks; (ii) DeSSE, an information-theoretically secure distributed storage model based on n × m fragmentation, ensuring zero information leakage below reconstruction thresholds; and (iii) a multi-tier data sovereignty model (C0–C3) enforcing policy-driven data locality and regulatory compliance across federated nodes. We formalize privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats, and demonstrate that the proposed architecture achieves information-theoretic confidentiality, forward secrecy, and attack-resilient distributed governance. A privacy leakage analysis shows that the probability of data reconstruction under sub-threshold compromise is identical to zero, outperforming conventional blockchain storage models based on encryption alone. Simulation and case study results indicate that Data Communities achieve up to 99.999% service availability, 55% reduction in external data exposure, and 22–35% carbon-aware optimization, while maintaining strict privacy guarantees across distributed environments. Compared with existing blockchain systems (e.g., Ethereum, Hyperledger Fabric), the proposed framework shifts privacy protection from access-control and pseudonymity to structural, information-theoretic privacy by design. Overall, the results establish Data Communities as a scalable and quantum-resilient foundation for next-generation privacy-preserving blockchain infrastructures, bridging distributed AI, secure storage, and cooperative governance under a unified formal model.
Muhammad Ali Hassan Ahmad, Muhammad Hashim Ali, Muhammad Ali Amer, Muhammad Naiman Jalil · 6 authors
A blockchain is a decentralized, secure ledger system that enables transparent and immutable record-keeping, essential for trust and security in digital transactions. Smart contracts are self-executing agreements encoded on a blockchain, enabling different parties to fulfill the terms of the agreement automatically. These contracts trigger corresponding actions when conditions are met, ensuring decentralized and transparent transactions. Writing reliable smart contracts is challenging due to the lack of standardization. To find security vulnerabilities, tools based on various approaches, including symbolic execution, are used. However, these tools often report a large number of false positives, raising concerns about their reliability. The time and effort spent investigating false positives diverts resources from addressing actual vulnerabilities. Therefore, such tools must also be evaluated according to the rate of false positives they exhibit. More importantly, the algorithms and heuristics used by the tools must be enhanced to distinguish between true vulnerabilities and false alarms. In this paper, we first demonstrate the prevalence of false positives in vulnerability reports generated by Mythril, a symbolic execution-based analysis tool for Ethereum smart contracts. We analyze the root causes of these inaccuracies and devise a rule-based approach based on the gained insight to reduce false positives. We implement our rules for the most impactful vulnerabilities in Mythril and assess the effectiveness of our approach. Our results show a significant reduction in false positives without compromising the detection of true vulnerabilities, thus enhancing the tool's reliability.
Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies. We study four Bitcoin and Ethereum ETPs traded on Xetra and Nasdaq Stockholm over the period January 2024 - December 2025 using one-minute bars. As a benchmark, we adopt an extreme value theory approach in which anomalous bars are defined as returns falling below a threshold estimated by fitting a generalised Pareto distribution to left-tail exceedances. We then propose three new binary anomaly indicators. The first, a cross-venue divergence anomaly, identifies venue-specific price divergence between the two exchanges. The second is a no-recovery anomaly that identifies extreme price drops followed by little or no recovery over the next ten active bars. The third is a momentum-reversal anomaly that identifies extreme price drops following positive short-term momentum. Although each anomaly type represents fewer than 1% of one-minute bars, statistical analysis using Mann-Whitney U tests shows that anomaly observations exhibit significantly higher effective spreads, higher values of liquidity-related ratios, and more pronounced order-flow imbalances than non-anomalous bars. Furthermore, employing an out-of-sample prediction methodology with four classifiers - random forest, logistic regression, extreme gradient boosting, and light gradient boosting machine - shows that all four anomaly types are predictable one bar ahead, with AUC-ROC values of up to 0.82. Permutation importance indicates that short-term volatility and drawdown measures are generally more useful for prediction than microstructure variables.
Stablecoins have rapidly emerged as an important class of digital assets and a component of the digital financial ecosystem. Despite their growing importance, the statistical properties of stablecoin transaction activity remain largely unexplored. To the best of our knowledge, this is the first study to investigate scaling behavior in stablecoin transaction data, focusing on USDT and USDC. We analyze approximately 370 million USDT and USDC transactions recorded on the Ethereum blockchain across six periods spanning June 2024 to February 2026. Based on interactions between Externally Owned Accounts (EOAs) and Smart Contracts (SCs), we classify transactions into four categories: EOA-EOA, EOA-SC, SC-EOA, and SC-SC. Using maximum-likelihood estimation of power-law exponents, we find that transaction value distributions exhibit heavy-tailed scaling for both stablecoins across all periods and interaction categories. We identify two distinct scaling regimes: EOA-involved categories cluster around 1.45-1.60, whereas SC-SC transactions exhibit higher exponents of approximately 1.72-1.73. Sensitivity analysis confirms that this separation is robust across periods, stablecoins, and fitting sample sizes. Counterfactual analysis shows that changes in category weights alone cannot explain the observed variation in the overall exponent. Across different sample sizes, the counterfactual path accounts for only about 10%-35% of the total temporal range observed in the actual data. Overall, our results indicate two broadly differentiated scaling regimes in the tail of stablecoin transaction values. Power-law tail behavior is observed throughout stablecoin transaction activity, but the exponent depends on whether transactions are driven by EOAs or SCs. These findings provide a basis for further research on scaling behavior and transaction heterogeneity in blockchain-based financial systems.
Velian Prapatoni, Rizky Parlika, Firza Prima Aditiawan
Cryptocurrency markets are characterized by high volatility, rapid price fluctuations, and substantial uncertainty, creating challenges for investment risk interpretation. This study develops a descriptive risk-interpretation framework, rather than a price-prediction or decision-optimization model, by integrating multi-scale volatility analysis with technical indicators. A quantitative descriptive design was applied to approximately one year of historical hourly price data for Bitcoin and Ethereum, covering open, high, low, close, volume, and percentage change attributes. The data were chronologically sorted, numerically cleaned and normalized, transformed into log returns, and analyzed through rolling standard deviation. Volatility was estimated across three explicitly defined horizons: short-term 7-period, medium-term 30-period, and long-term 90-period rolling windows. Moving Average (MA), Relative Strength Index (RSI), and Average True Range (ATR) were then incorporated to contextualize trend direction, momentum, and fluctuation intensity. The results show that volatility is strongly horizon-dependent: short-term movements responded more sharply to market shocks, whereas longer horizons produced smoother risk patterns. Across the analyzed Bitcoin and Ethereum hourly series, the reported 42.3% short-term and 21.7% medium-term increases were calculated as relative differences against long-term rolling volatility during identified high-uncertainty intervals, not as predictive accuracy measures. These findings indicate that combining rolling volatility with MA, RSI, and ATR can improve the transparency of descriptive cryptocurrency risk assessment. The framework may support preliminary interpretation for novice or risk-averse investors, although it does not empirically test investor comprehension or subsequent decision quality.
Abstract This paper presents a provenance-preserving deep-learning framework for computational design screening and full-wave validation of high-isolation millimeter-wave cylindrical dielectric resonator antennas in a two-port MIMO configuration. The framework integrates Ansys HFSS, Python-based sampling, deep-learning prediction, and blockchain- and IPFS-based provenance within a reproducible computational workflow. An HFSS-Python pipeline generates 300 parametric CDRA geometries, each represented through binary image encoding and associated electromagnetic response data, including the reflection coefficient ( $$S_{11}$$ ) and the HFSS-exported total radiated electric-field response ( $$r_E$$ ). CNN, LSTM, CNN–LSTM, and multimodal Image–CNN–LSTM models are trained to predict radiation behaviour and rank candidate designs through pseudo-ECC-based screening. The selected CDRA is subsequently assessed through full-wave HFSS simulation in an orthogonal MIMO arrangement. At 27.96 GHz, the validated design achieves an $$S_{11}$$ of $$-20.79$$ dB, an $$S_{21}$$ of $$-50.13$$ dB, an ECC of $$7.78\times 10^{-8}$$ , diversity gain close to 10 dB, TARC of $$-21.09$$ dB, and CCL of 0.02185 bits s $$^{-1}$$ Hz $$^{-1}$$ . The orthogonal layout also improves isolation by about 20.31 dB compared with the parallel arrangement. The generated simulation and learning artefacts are further registered through a local Ethereum development network integrated with IPFS to support authenticity, traceability, and tamper-evident record keeping. The study demonstrates a practical computational route for automated and verifiable antenna-design screening, linking data-driven optimization with provenance-preserved management of electromagnetic design artefacts.
Novan Ilham Ramadhan, Rizky Parlika, Ardhon Rakhmadi
Electronic voting (e-voting) systems continue to face challenges related to transparency, result validation, and duplicate voting prevention. Blockchain technology offers characteristics such as decentralization, transparency, and immutability that can support more auditable voting processes. This study presents a prototype implementation of a blockchain-based e-voting system using Ethereum smart contracts to support transparent vote recording, public auditability, and wallet-level double voting prevention. The system was implemented using Solidity-based smart contracts deployed on the Ethereum Sepolia Testnet and integrated with MetaMask for transaction authentication. Smart contracts manage election creation, candidate registration, voter registration, vote recording, duplicate vote prevention, and voting result finalization. An audit trail mechanism was implemented to allow voting activities and transaction records to be publicly verified through the Etherscan blockchain explorer. All predefined functional testing scenarios were executed successfully. The evaluation results indicate an average gas usage of 75,109 gas, an estimated transaction cost of 0.000113 ETH, and a transaction latency of approximately 4–5 seconds. The implemented wallet validation mechanism and hasVoted mapping effectively prevent duplicate voting attempts associated with the same wallet address. However, the proposed system represents a prototype-level evaluation conducted on the Ethereum Sepolia Testnet and does not provide voter identity verification, ballot anonymity, or real-world election readiness. The findings demonstrate the feasibility of Ethereum smart contracts for transparent auditability and wallet-level double voting prevention in blockchain-based voting environments.