Τα τελευταία χρόνια, οι Αποκεντρωμένες Εφαρμογές (Decentralized Applications - DApps) γνωρίζουν σημαντική ανάπτυξη, και η ικανότητά τους να διαχειρίζονται ψηφιακά περιουσιακά στοιχεία υψηλής αξίας έχει οδηγήσει σε σημαντική καινοτομία σε τομείς όπως η αποκεντρωμένη χρηματοοικονομική (Decentralized Finance - DeFi), η διακυβέρνηση (governance) και η διαχείριση της εφοδιαστικής αλυσίδας (supply chain management), με τη δημιουργία διαφόρων δικτύων blockchain για την κάλυψη της ζήτησης για τέτοιες υπηρεσίες. Παράλληλα, έχουν αναπτυχθεί διάφορες μεθοδολογίες για την προστασία αυτών των δικτύων από κακόβουλους παράγοντες (malicious actors) που επιχειρούν να εκμεταλλευτούν αδυναμίες (vulnerabilities) που υπάρχουν στα έξυπνα συμβόλαια (smart contracts) τα οποία εκτελούν μια προκαθορισμένη επιχειρηματική λογική (business logic), με σκοπό να κλέψουν μεγάλα χρηματικά ποσά μέσω αυτών. Αν και το οικοσύστημα του Ethereum επωφελείται από μια ώριμη σουίτα εργαλείων ασφαλείας, αυτά είναι κυρίως σχεδιασμένα για τον εντοπισμό συντακτικών αδυναμιών (syntactic vulnerabilities), παραλείποντας συχνά σφάλματα που προκύπτουν από την απόκλιση μεταξύ του επιδιωκόμενου σχεδιασμού ενός έξυπνου συμβολαίου και της υλοποίησής του στην αλυσίδα (on-chain implementation), επιτρέποντας έτσι στους επιτιθέμενους να χειραγωγήσουν τη λειτουργικότητα του συμβολαίου για κακόβουλο όφελος. Νέα δίκτυα blockchain και γλώσσες προγραμματισμού, όπως το δίκτυο Sui και η γλώσσα του Sui Move, έχουν δημιουργηθεί προσφέροντας νέες δυνατότητες και χαρακτηριστικά, αλλά ταυτόχρονα εισάγουν νέες κατηγορίες κινδύνου. Ορισμένα παραδείγματα είναι η διαρροή δυνατοτήτων (capability leakage) και οι παραβιάσεις του προτύπου μάρτυρα (witness pattern violations), οι οποίες είναι αόρατες στις παραδοσιακές ταξινομίες ασφαλείας που βασίζονται στο Ethereum, λόγω των διαφορετικών υποδομών και προγραμματιστικών μοντέλων. Η πρόληψη τέτοιων επιχειρηματικών αδυναμιών (business vulnerabilities) απαιτεί κατάλληλη τυπική μοντελοποίηση και επαλήθευση (formal modeling and verification) της επιδιωκόμενης επιχειρηματικής διαδικασίας εντός των έξυπνων συμβολαίων, διασφαλίζοντας ότι όλες οι πιθανές αλληλεπιδράσεις παραμένουν συνεπείς με την αναμενόμενη συνολική συμπεριφορά του συστήματος. Η παρούσα έρευνα αντιμετωπίζει αυτό το πρόβλημα αναπτύσσοντας ένα τυπικά θεμελιωμένο, καθοδηγούμενο από οντολογίες πλαίσιο ανάλυσης ασφάλειας (formally grounded, ontology-driven security analysis framework) ειδικά για τη γλώσσα Sui Move, κωδικοποιώντας τις σημασιολογικές σχέσεις μεταξύ των δομών κώδικα (code constructs) της Sui Move, των προτύπων ασφαλείας (security patterns) και των κατηγοριών αδυναμιών. Για την επίτευξη αυτού του στόχου, η παρούσα διατριβή ακολουθεί τη μεθοδολογία Design Science Research (DSR), προκειμένου να γεφυρώσει το χάσμα μεταξύ της αρχιτεκτονικής πρόθεσης υψηλού επιπέδου (το «γιατί» - the why) και των ελαττωμάτων κώδικα χαμηλού επιπέδου (το «πώς» - the how). Τα συμπεράσματα που προέκυψαν από μια συστηματική μελέτη χαρτογράφησης (systematic mapping study) και τη σύγκριση των γλωσσών προγραμματισμού Solidity και Sui Move χρησιμοποιούνται για τη δημιουργία δύο τεχνουργημάτων (artifacts): 1) ενός οντολογικού πλαισίου έξι επιπέδων (six-layer ontological framework) για τη Sui Move και 2) ενός εργαλείου ανάλυσης (Sui Move Analyzer). Όσον αφορά το οντολογικό πλαίσιο, περιλαμβάνονται η χαρτογράφηση γραμματικής (grammar mapping), η ταξινόμηση ασφαλείας, τα αρχιτεκτονικά πρότυπα και η τυπική μοντελοποίηση συμπεριφοράς (formal behavioral modeling), σε συνδυασμό με τη δημιουργηθείσα ταξινόμηση Sui-Unified Weakness Classification (SUWC), η οποία κατηγοριοποιεί τα ελαττώματα που σχετίζονται ειδικά με την πλατφόρμα (platform-specific defects) σε τέσσερις ομάδες, ευθυγραμμισμένες με μια βιβλιοθήκη τεσσάρων επαληθευμένων σχεδιαστικών προτύπων ασφαλείας (security design patterns) της ενσωματωμένης οντολογίας. Όσον αφορά το δεύτερο τεχνούργημα, αυτό αναπτύχθηκε για να αξιολογήσει την πρακτική χρησιμότητα του οντολογικού πλαισίου, χρησιμοποιώντας μια αρχιτεκτονική διπλής ροής (dual-pipeline architecture) που συνδυάζει την παραδοσιακή εξαγωγή ευρετικών κανόνων (heuristic extraction) με την οντολογική συλλογιστική που βασίζεται σε SPARQL (SPARQL-based ontological reasoning). Χρησιμοποιώντας αυτή τη μεθοδολογία, ο αναλυτής μπορεί να εντοπίσει κινδύνους σε σημασιολογικό επίπεδο (semantic-level risks), ενώ παράλληλα βοηθά τους προγραμματιστές προτείνοντας αυτοματοποιημένες αποκαταστάσεις βασισμένες σε πρότυπα (pattern-based remediations), οι οποίες βασίζονται σε καθιερωμένα παραδείγματα ασφάλειας (security paradigms). Η αξιολόγηση των τεχνουργημάτων ακολουθεί το Framework for Evaluation in Design Science (FEDS), συνδυάζοντας τεχνητή αθροιστική αξιολόγηση (artificial summative evaluation) μέσω ειδικά κατασκευασμένων συμβολαίων με γνωστή αντικειμενική αλήθεια (ground truth), και φυσιοκρατική αθροιστική αξιολόγηση (naturalistic summative evaluation) μέσω της ανακατασκευής μιας πραγματικής εκμετάλλευσης (exploit reconstruction), προκειμένου να διασφαλιστεί τόσο η εσωτερική όσο και η εξωτερική εγκυρότητα (internal and external validity). Σε 14 συμβόλαια Sui Move, 42 περιπτώσεις δοκιμών (test cases) και ένα σενάριο εκμετάλλευσης (exploit scenario), χρησιμοποιούνται 13 ποσοτικές μετρικές που καλύπτουν την ορθότητα (precision, recall, F1-score), την κάλυψη (taxonomy and pattern completeness) και την πρακτική χρησιμότητα (false-positive rate, runtime performance). Η εγκυρότητα και των δύο τεχνουργημάτων αξιολογείται επιπλέον σε πέντε διαστάσεις (μέσου, τεχνική, σχεδιασμού, σκοπού και γενίκευσης - instrument, technical, design, purpose, and generalization), επιβεβαιώνοντας ότι το πλαίσιο αποδίδει σταθερά σε όλες τις στοχευμένες διαστάσεις εγκυρότητας και πληροί την απαιτούμενη αυστηρότητα (rigor) για να κλείσει επαρκώς τον κύκλο DSR.
Prof. Suvarna A. Bahir, Tejas Vaidya, Ranjeet Waghmode, , Abhishek Gavand, · 5 authors
Electronic voting systems have gained significant attention due to their ability to improve the efficiency and accessibility of elections. However, traditional voting methods and centralized electronic voting systems face challenges such as vote tampering, lack of transparency, unauthorized access, and delayed result generation. Blockchain technology offers a decentralized and secure solution to address these limitations. This paper presents a Secure Blockchain-Based E-Voting System Using Smart Contracts that leverages Ethereum blockchain technology to provide transparent, secure, and tamper-resistant elections. The proposed system integrates voter authentication, election management, candidate registration, vote recording, and real-time result monitoring within a single platform. Smart contracts developed using Solidity are used to automate election operations and ensure the integrity of voting transactions. The system is implemented using HTML, CSS, JavaScript, FastAPI, MySQL, Ethereum, Ganache, and MetaMask. Votes are securely recorded on the blockchain, preventing unauthorized modifications and improving election transparency. The proposed framework enhances voter trust, reduces dependency on centralized authorities, and simplifies election management. This solution can be effectively used for academic institutions, organizations, and small-scale election environments requiring secure and reliable voting processes. Keywords: Blockchain, Electronic Voting, Ethereum, Smart Contracts, Solidity, Decentralized Voting.
Energy demand in urban and metropolitan regions has been growing rapidly, often exceeding production capacity, leading to imbalances in energy distribution. Existing peer-to-peer (P2P) energy trading models, along with classical algorithms like FCFS and best-fit frameworks in smart cities, address some of these issues. However, they often face challenges such as limited transaction success percentage, inefficiencies in price matching, and privacy concerns during trades. To overcome these limitations, a framework is proposed that integrates game-theoretic pricing-based collaborative trading with Nash equilibrium and an additional pricing mechanism (CoGap) to enhance fairness and transaction success percentage in decentralized energy markets. The proposed framework is implemented on an Ethereum-based blockchain using Solidity smart contracts, incorporating cryptographic security through the Keccak-256 hash function and privacy-preserving zero-knowledge proofs (ZKPs). Moreover, it ensures security and price negotiations while maximizing transaction efficiency. Simulation results demonstrate that CoGap consistently achieves higher transaction success rates compared to four state-of-the-art collaborative energy trading schemes.
Transaction monitoring and efficient audit management have become increasingly importantin modern financial systems. Traditional centralized databases and auditing methods often face challengesrelated to security vulnerabilities, fraudulent activities, and data manipulation. A blockchain-based financialtransaction monitoring system integrates smart contracts, decentralized ledgers, and audit trails to automatefinancial operations, enhance transparency, and reduce fraud risks. The proposed architecture is implementedon Ethereum and Hyperledger Fabric platforms, enabling automated transaction validation and executionthrough smart contracts. All transactions are stored in an immutable decentralized ledger, while audit trailsare generated and maintained automatically. Simulation results demonstrate a 40–60% reduction in fraudulentactivities and up to a 70% decrease in audit processing time compared with conventional approaches. Theapplication of cryptographic algorithms and Zero-Knowledge Proofs further strengthens data security andprivacy protection. The proposed solution contributes to the improvement of financial control and auditingsystems within the framework of the digital economy.
K Vigneshkumar, A R JayaSudha, P Nandini, Jana Murugesan · 7 authors
The system architecture presented in this work uses blockchain technology in conjunction with cryptographic authentication methods and real-time anomaly detection to validate the integrity of artificial intelligence models. By integrating tokenization-based model tracking, zero-knowledge proof verification, and machine learningbased integrity monitoring, the suggested solution fills important holes in current methods. We use Ethereum smart contracts to construct a prototype system and assess it using several AI model designs. In comparison to signature-based methods alone, experimental findings show 96.7% fewer false positives, sub-second verification latency for the real-time component, and 99.4% detection accuracy for model tampering attempts. Every day, the system effectively processes 10,000 model inference records while upholding cryptographic security requirements.
Execution Tickets (ET) have emerged as a leading proposal for mitigating MEV-related centralization risks by internalizing MEV through a protocol-level lottery system. This paper provides an empirical game-theoretic analysis (EGTA) of the ET mechanism under an infinite-supply design, modeled as a Tullock contest. We evaluate a 2-slot lookahead window as a minimal temporal design that limits multi-slot MEV while preserving support for user pre-confirmations. By introducing a forfeiture parameter, we parameterize a continuum between All-Pay and Winner-Pay regimes. We then map the fairness-revenue frontier, revealing a fundamental design tension: higher contest decisiveness and forfeiture rates can improve protocol revenue, but may reduce allocation fairness by entrenching dominant builders. We identify a quantitative Goldilocks zone that balances MEV-capture efficiency with market diversity.
Ethereum is a widely adopted blockchain platform that supports a large number of decentralized applications. Despite its rapid growth, Ethereum remains vulnerable to security threats, particularly phishing attacks that exploit transactional behavior. This study investigates the effectiveness of tree-based ensemble learning models for detecting phishing transactions on the Ethereum network using an imbalanced transaction dataset. Seven tree-based ensemble classifiers are empirically evaluated under a cost-sensitive learning framework, with performance assessed using the Matthews Correlation Coefficient (MCC) as the primary metric. The results show that boosting-based ensembles substantially outperform bagging-based approaches and a single decision tree. In particular, Gradient Boosting achieves the strongest detection performance with an MCC of 0.9742, while CatBoost provides a trade-off between detection performance and computational efficiency, achieving competitive detection accuracy with the lowest average inference time (approximately 1.54 µs per transaction). The findings demonstrate that accurate and robust phishing detection can be achieved using a compact feature representation, enabling practical deployment with reduced computational overhead.
Cryptocurrency fraud on blockchain platforms continues to cause substantial financial losses, creating an urgent need for detection systems that are not only accurate but also interpretable for operational and regulatory use. In this paper, we propose an explainable framework for Ethereum fraud detection integrating an XGBoost ensemble with TreeSHAP. This system achieves high predictive performance (96.3% F1-score, 96.6% recall) while providing model-level transparency via an interactive chatbot interface. Evaluation using fidelity and stability metrics confirms the reliability of the SHAP-based insights, while user-role simulations demonstrate that our structured delivery enhances clarity and actionability over standard visualizations. This work offers a practical, transparent foundation for deploying robust AI in high-risk financial environments without sacrificing accuracy.
NFT (Non-Fungible Token), son yıllarda kripto varlık ekosisteminde önemli bir dönüşüm yaratmış dijital varlıklardır. Dijital sanat eserlerinden koleksiyonluk eşyalara, oyun içi varlıklardan sanal gayrimenkullere kadar birçok alanda kullanılmakta olup, dijital içeriklerin özgünlük ve sahiplik niteliklerini kripto varlık biçiminde temsil etmektedir. Bu çalışmada NFT kavramı, tarihsel gelişimi, kullanım alanları ve türleriyle, NFT’lerin güvenilirliğini sağlayan Blokzincir, ikinci nesli Ethereum ile akıllı sözleşmeler gibi teknik yapısı üzerinde durularak hukuki niteliği konusunda değerlendirmeler yer almaktadır.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
Cryptographic wallets play a vital role in securing digital assets within blockchain networks by managing private keys that authorize secure transactions. However, side channel analysis (SCA) attacks have become a serious threat, enabling attackers to extract sensitive information by exploiting algorithmic weaknesses in microcontroller-based wallets, resulting in the loss of millions of dollars in digital assets. In hierarchically deterministic (HD) systems, the compromise of a single primary key can endanger all subsequent child keys, while the use of independent keys for each account introduces complexity and challenges in key management. This work presents HardVault, a field programmable gate array (FPGA)-based cryptocurrency wallet that supports both Bitcoin and Ethereum. HardVault introduces the first hardware wallet architecture that implements both non-deterministic (ND) and HD key generation modes directly in hardware, giving users the flexibility to choose either approach based on their security and usability needs. By leveraging constant-time operations and hardware-enforced private-key isolation, the design significantly improves resilience to SCA attacks. In addition, the architecture prioritizes resource efficiency to minimize area usage without compromising security, making it well-suited for compact, portable hardware wallet applications. Implementation on a ZCU104 FPGA shows that HardVault uses only 27% of available look-up tables (LUTs). Compared to the Trezor One cryptocurrency (crypto) wallet, the proposed implementation achieves$9\times $higher energy efficiency,$8\times $lower latency, and$7\times $higher throughput.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
This report synthesises findings from 3 peer-reviewed papers addressing the following research question: How does the integration of persistent homology-based topological features in MTGCL compare to other graph contrastive learning methods (e.g., GTCL, GCMC) in terms of anomaly detection accuracy and. Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the integration of persistent homology-based topological features in MTGCL compare to other graph contrastive learning methods (e.g., GTCL, GCMC) in terms of anomaly detection accuracy and inference latency on large-scale Ethereum transaction datasets? Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.
In July 2021, the electronic musician and scholar Holly Herndon launched Holly+, a vocal deep fake modeled on her singing voice. Holly+ is powered by an artificial intelligence engine trained on recordings of Herndon’s voice. Musicians were welcome to use the transformed audio commercially and simply credit Herndon and Holly+. They also could release their work with the official imprimatur of a Decentralized Autonomous Organization (DAO), a Web 3.0 entity tied to the Ethereum blockchain. My paper situates the Holly+ project against the backdrop of intellectual property law in the US, both as it stood in the late 2010s and 2020s and in light of the legacy of legally sanctioned appropriation and extraction of work by Black performers. The DAO framework Holly+ uses is unlikely to gain traction, and Herndon and Mat Dryhurst’s provocative invocation of “identity play” suggests an uneasy relationship to the racialized legacy of mimicry and impersonation in the history of American popular music. But the project stands as an important attempt at creating an artist-driven, grassroots effort at monetizing, authenticating, and protecting the intellectual property of musicians in the era of machine learning.
Bitcoin, Ethereum, and Algorand blockchains are described in this chapter. The Bitcoin blockchain was initially designed by Satoshi Nakamoto in 2008-2009. This created a revolution which is still ongoing. Its premier application was the Bitcoin cryptocurrency.