Blockchain Papers

Alessandro Bigiotti, Leonardo Mostarda, Alfredo Navarra, Davide Sestili

No abstract is available for this record.

InvestTWLGF Oy

<p>This Zenodo record contains the verified smart contract source code for the TWLGF token project.</p> <p>The code has been published and verified on BscScan as an Exact Match. <br>This record provides an EU OpenAIRE DOI and long-term archive reference for the official contract source.</p> <p>Network: BNB Smart Chain (BSC)<br>Compiler: solc v0.5.16+commit.9c3226ce<br>Optimization: 200 runs<br>License: MIT</p> <p>Verified contract address:<br>https://bscscan.com/address/0xA45D0f9337eF5539d77c41e968137C391d1d7704#code</p> <p>This record complements other publicly archived TWLGF documentation:<br>• Internet Archive: https://archive.org/details/twlgf-bsc-scan-verified-source-2025-11-12<br>• GitHub repository: https://github.com/twlgfofficial/twlgf_logo<br>• Google Drive timestamped PDF (2025-11-12)<br>• Whitepaper DOI: https://zenodo.org/records/17516133</p>

Uchit Shubhangi, P. Tejaswini, Umbarkar Ishwari, Nagude Vaishnavi · 5 authors

In today’s educational landscape the proliferation of forged or manipulated student certificates undermines trust in academic credentials. This paper presents a blockchain-based solution for issuing and validating student certificates by recording cryptographic hashes of credential metadata on a distributed ledger and optionally storing full certificate files off-chain. Smart contracts govern issuance and verification, eliminating intermediaries and enabling instant, tamper-proof checks. The proposed system enhances transparency and integrity, reduces verification overhead, and empowers students and employers with direct access to credential authenticity. Challenges such as scalability, privacy of student data, cost of transactions, and institutional adoption are discussed with suggestions for future work.

Arman Petrosyan

The paradigm of enterprise analytics is undergoing a fundamental shift from centralized, reactive reporting to distributed, proactive intelligence. This review article evaluates the integration of machine learning models within SAP business intelligence frameworks operating across multi-cloud and hybrid environments. We analyze how the transition toward a federated data architecture, facilitated by SAP Datasphere, enables the deployment of high-performance neural networks without the traditional constraints of data replication. The study specifically examines the efficacy of Long Short-Term Memory units for temporal forecasting in SAP Integrated Business Planning and the role of unsupervised learning models in real-time financial anomaly detection. Furthermore, we explore the rise of augmented analytics and natural language processing in democratizing data access, alongside the operational necessity of MLOps to mitigate model drift in volatile global markets. The review also addresses critical technical and strategic barriers, including data latency across distributed cloud nodes, the harmonization of structured and unstructured data, and the evolving landscape of global data sovereignty. By synthesizing current performance benchmarks with future directions such as agentic intelligence and the integration of carbon accounting through the green ledger, this research provides a roadmap for architecting autonomous analytical ecosystems. We conclude that the convergence of machine learning and distributed cloud infrastructure is the primary catalyst for transforming raw enterprise data into a strategic, self-optimizing asset.

Poonna Yospanya, Siwat Sabprasan, Vacharapat Mettanant

This paper introduces a decentralized and configurable framework for will execution that integrates blockchain technology, smart contracts, and Shamir's Secret Sharing to provide a secure, transparent, and adaptable inheritance process. Unlike traditional systems that rely entirely on centralized intermediaries, our framework allows for flexible access control, enabling the inclusion or exclusion of trusted third parties based on the specific requirements of the testator and beneficiaries. The will document is encrypted, and the decryption key is distributed among designated participants using multilevel secret-sharing techniques. A blockchain-based smart contract autonomously governs the execution process, ensuring that predefined conditions, such as the testator's death, are verified before controlled access to the encrypted document is granted. By supporting threshold-based and hierarchical access structures, the system is adaptable to diverse inheritance scenarios. Experimental results demonstrate its scalability and efficiency in a blockchain environment, showcasing its potential as a secure and versatile solution for digital inheritance.

Yufeng Li, Yang Li, Jing Nie, Sezai Ercisli

With the deep integration of 6G, the Internet of Things, and artificial intelligence, this paper proposes an intrusion detection and defense framework that combines robust AI kernel reconstruction, a cross-layer collaborative perception architecture, and a dynamic defense closed-loop mechanism to address advanced persistent threats and dynamically evolving attacks targeting next-generation consumer services. First, a lightweight detection model ATF-KDBC is designed based on adversarial training and online knowledge distillation. Gradient masking and noise injection are employed to enhance robustness against adversarial samples, while a drift-aware module enables adaptive optimization under concept drift scenarios. The model achieves accuracies of 99.25% and 99.84% on the NSL-KDD and IoT-23 hybrid datasets, respectively, and compresses the model size to 1.08 MB, representing a 97.6% reduction compared with the BERT teacher model. Second, a multidimensional attack chain analysis model is developed based on a STHGN. By integrating semantic, structural, and temporal features with a multi-head self-attention mechanism, the model enables cross-layer threat tracing and millisecond-level response, achieving an F1-score exceeding 97.0% on the DARPA dataset. Furthermore, this study explores the construction of a distributed CTIS network by integrating federated learning and blockchain technology. Zero-knowledge proofs are employed to ensure privacy preservation, while a Quality of Data and Quality of Model scoring mechanism enables efficient and precise deployment of defense strategies. Experimental results demonstrate that the proposed framework significantly outperforms traditional methods in terms of robustness, environmental adaptability, and computational efficiency, thereby providing both theoretical support and a technical pathway for enhancing the resilience and security of next-generation consumer services.

Harshith Sai Veeraiah, Syed Badruddoja, Ram Dantu

Smart contract vulnerabilities hinder the development of decentralized finance (DeFi) applications due to overarching attacks and their impact on financial transactions. While rule-based static analysis tools can detect common exploits, they often fail to uncover subtle or rapidly evolving vulnerabilities. Moreover, dynamic analysis techniques over-rely on patterns, limiting token representation and explainability of attack detection. We introduce a novel architecture that unites Abstract Syntax Trees (ASTs) with a transformer-based deep learning framework to improve the detection of vulnerable smart contracts. By encoding Solidity-based smart contracts into ASTs, the structural context essential for capturing complex code dependencies is retained. Furthermore, the transformer model captures the context, dependencies, and semantics of vulnerabilities. Our performance evaluations show that the AST-transformer-based vulnerability detection method improved the detection rate precision by 4% compared to RNN, LSTM, GNN and vanilla transformer-based detection techniques. Additionally, we use SHapley additive explanation to determine the contribution of each to explain and reason the vulnerability detection. Moreover, we use saliency maps (heatmaps) to identify the line of code that is attributed to vulnerability detection.

Chandni Patel, Parth Sheth, Megh H. Shah, Dev Mehta · 8 authors

One of the main issues with the Industrial Internet of Things (IIoT) in V2X communication is the threat of attacks. A comprehensive Intrusion Detection System (IDS) and a transparent ledger are important for providing an Intelligent Transportation System (ITS) beyond 5G. However, another major problem is that it is centralized and lacks a clear explanation of traditional IDS. By integrating Federated Learning (FL) to make it distributed and Explainable AI (XAI) to add a brain to the black box model to add the explanation factor, we make the model more robust and suitable for real-life situations. In this approach, we experimented using the X-IIOTID dataset. This dataset is a real-time indicator of the attack in an IIoT network such as V2X. It provides difficult and real-time scenarios that highlight the complexity of IDS. Furthermore, the benign data the model classifies is stored in the blockchain to make the system secure and transparent. Our FL-XAI-based technique provides an accuracy of $98 \%$ results than previous models. The proposed approach provides a clear and brief view of factors that affect classification actions, which helps users make security decisions. Evaluation of Pravah based on latency, accuracy, precision, recall, F1-score, and ROC-AUC confirms its effectiveness. This study contributes towards a more secure and interpretable ITS, bridging the gap between model performance and real-world applicability.

Ismail Jirou, Ikram Jebabli, Mohammad Isleimeyyeh, Elie Bouri

No abstract is available for this record.

Kexin Liu, Viktor Manahov, Dimitrios Stafylas

Our study, set against the backdrop of the ongoing Russia-Ukraine war, Hamas-Israel conflict and fintech advancements, investigates the implications of Bitcoin (BTC), Ethereum (ETH), and Tether (USDT)'s active addresses on BTC prices. In doing this, we employ a combination of traditional time series VAR model and machine learning BPNN model during the geopolitical conflict period. Our findings indicate that BTC and ETH prices often move in tandem ahead of geopolitical conflicts. Investors aiming to boost their income choose to buy lower-priced ETH, leading to a rise in the number of active ETH addresses, positively correlated with BTC's price. However, during geopolitical conflicts, this relationship shifts. The number of active USDT addresses is a significant factor influencing BTC price. Consequently, when confronted with a steep decline in BTC prices, investors tend to convert BTC into the USDT stablecoin to avert losses.

Poliana Kálida Andrade da Costa

A rápida expansão das criptomoedas transformou o cenário financeiro mundial ao introduzir novas formas de transação econômica baseadas em tecnologia digital descentralizada. No Brasil, o crescimento do uso do Bitcoin despertou atenção do meio jurídico devido ao potencial emprego desse ativo virtual em operações de ocultação patrimonial ilícita. O contexto impôs desafios regulatórios e investigativos ao ordenamento jurídico, exigindo respostas normativas para prevenir crimes financeiros digitais. O objetivo do estudo foi analisar a adequação do ordenamento jurídico brasileiro frente aos desafios impostos pela utilização do Bitcoin como instrumento de lavagem de dinheiro, especialmente após a promulgação da Lei 14.478/2022. A pesquisa utilizou abordagem qualitativa, método dedutivo e levantamento bibliográfico e documental, com base em doutrina, legislação e análise jurisprudencial. Antes da Lei nº 14.478/2022, o Poder Judiciário responsabilizava agentes envolvidos em crimes com criptoativos com fundamento na Lei nº 9.613/1998. Com o novo marco regulatório, houve fortalecimento de mecanismos de controle e rastreabilidade e ampliação do dever de cooperação de exchanges. A jurisprudência do STJ e do TRF-3 consolidou entendimento de que Bitcoin possui conteúdo econômico e pode ser objeto de medidas assecuratórias e responsabilização de intermediadoras digitais. O ordenamento jurídico brasileiro encontra-se em processo de adequação progressiva para responder a riscos jurídicos e financeiros vinculados ao uso ilícito de criptomoedas.

Walter Balzano, Pasquale Miranda

No abstract is available for this record.

Yuxin Liu, Stephen Chan, Jeffrey Chu, Yuanyuan Zhang · 5 authors

Fraudulent activity on blockchain networks poses significant risks to the integrity and trust of decentralized finance ecosystems. The timely and accurate detection of fraud nodes such as phishing addresses within large-scale Ethereum transaction networks remains a major challenge due to their dynamic, sparse, and evolving structures. While methods like graph deep learning (e.g., graph neural networks) have been extensively explored, they are not inherently designed to capture higherorder interactions and textual information embedded within graph data. Motivated by the urgent need for advanced and robust fraud detection techniques, we introduce a novel graph prompting method named Large Language Model-Simplicial Complex (LLM-SC) based graph prompting framework that leverages LLM-based multi-agent collaboration system, LLMbased financial news prompt function, and simplicial neural networks to capture both the structural and contextual dimensions of blockchain activity. The empirical studies demonstrate the effectiveness of our approach, and these results provide a new tool for blockchain analytics platforms and regulatory authorities, enabling earlier and more accurate identification of fraudulent behavior and ultimately supporting safer and more resilient digital financial systems. The code is available at https://github.com/y13564/LLM-SC.

Tejas Sharma, Ashish Kundu

Random numbers are basic building blocks for cryptography. For example, they are heavily utilized in Decentralized Finance (DeFi) and blockchain applications. Cryptographers and practitioners frequently employ bit selection, arithmetic, and logical operations to generate cryptographically secure random numbers (CSPRNs), thereby achieving the desired level of entropy and security. There is a need to analyze the security of such operations on CSPRNs. In this paper, we have studied and analyzed the security properties of arithmetic and some string operations on CSPRNs, and reviewed Boolean logic operations with a focus on the preservation or loss of entropy. We have analyzed and presented several proofs of security or lack of it for such operations. We have implemented and conducted experiments to corroborate these results using the NIST test suite. Our work applies not only to classical random numbers but also to quantum random numbers.

Ebuka Chinaechetam Nkoro, Love Allen Chijioke Ahakonye, Dong‐Seong Kim

Smart Contracts (SCs), which are the backbone of automated transactions and digital assets within the Metaverse, ironically suffer from their own share of security vulnerabilities. While detecting these SC vulnerabilities using Artificial Intelligence (AI) and Deep Neural Networks (DNNs) has demonstrated remarkable performance and gained wide adoption, a critical limitation remains: the lack of explainability in these black box models. To facilitate meaningful progress in this field, our study addresses this gap by introducing a model-agnostic explanation framework that is both visual and quantitative, with human stakeholders actively involved to govern, verify, and interpret SC model predictions. The explainable SC outputs can be utilized for reward issuance and digital assets governance in the Metaverse. The effectiveness of our proposed Explainable AI (XAI) approach is validated using benchmark datasets, BCCC SCsVul 2024 and BCCC SCsVul 2023, comprising Ethereum SC entropy source codes, where it achieves an optimal detection accuracy of 97.13% alongside comprehensive explainability. To the best of our knowledge, this represents the first attempt at making Ethereum SC vulnerability detection within the Metaverse explainable, offering a valuable foundation for blockchain researchers, Metaverse security experts, and practitioners seeking verifiable, trustworthy, and auditable Ethereum SC vulnerability detection.

Muhammad Ilman Abidin, Ahmad M. Ramli, Laina Rafianti, Gautam Kumar Jha

Investment in Non-Fungible Tokens (NFTs) is rapidly emerging in Indonesia, presenting both opportunities and challenges for the digital creative industry. As unique crypto assets, NFTs enable new ways to own and trade digital and physical goods, but current regulations, including the Commodity Futures Trading Law and Bappebti guidelines, do not fully address these transactions, creating legal gaps and increasing risks of fraud, money laundering, and market manipulation. Despite this, NFT communities like the Superlative Secret Society in Bali, supported by the Ministry of Creative Economy, have fostered creativity and economic activity. This study employs a normative juridical and comparative law approach to explore legal theories suitable for protecting NFT investments, finding that frameworks based on Ahmad M. Ramli’s transformative law and Mochtar Kusumaatmadja’s developmental law can ensure legal certainty, security, and fairness. The study concludes that comprehensive legal reforms are essential to safeguard investors and sustain the growth and international competitiveness of Indonesia’s digital creative industry.

Parsa Hedayatnia, Tina Tavakkoli, Hadi Amini, Mohammad Allahbakhsh · 5 authors

Smart contracts concentrate high value assets and complex logic in small, immutable programs, where even minor bugs can cause major losses. Existing taxonomies and tools remain fragmented, organized around symptoms such as reentrancy rather than structural causes. This paper introduces an attack-centric, program-structure taxonomy that unifies Solidity vulnerabilities into eight root-cause families covering control flow, external calls, state integrity, arithmetic safety, environmental dependencies, access control, input validation, and cross-domain protocol assumptions. Each family is illustrated through concise Solidity examples, exploit mechanics, and mitigations, and linked to the detection signals observable by static, dynamic, and learning-based tools. We further cross-map legacy datasets (SmartBugs, SolidiFI) to this taxonomy to reveal label drift and coverage gaps. The taxonomy provides a consistent vocabulary and practical checklist that enable more interpretable detection, reproducible audits, and structured security education for both researchers and practitioners.

Zexu Wang, Jiachi Chen, Zewei Lin, Wenqing Chen · 10 authors

Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability. However, lacking checks on signature usage conditions can lead to repeated verifications, increasing the risk of permission abuse and threatening contract assets. We define this issue as the Signature Replay Vulnerability (SRV). In this paper, we conducted the first empirical study to investigate the causes and characteristics of the SRVs. From 1,419 audit reports across 37 blockchain security companies, we identified 108 with detailed SRV descriptions and classified five types of SRVs. To detect these vulnerabilities automatically, we designed LASiR, which utilizes the general semantic understanding ability of Large Language Models (LLMs) to assist in the static taint analysis of the signature state and identify the signature reuse behavior. It also employs path reachability verification via symbolic execution to ensure effective and reliable detection. To evaluate the performance of LASiR, we conducted large-scale experiments on 15,383 contracts involving signature verification, selected from the initial dataset of 918,964 contracts across four blockchains: Ethereum, Binance Smart Chain, Polygon, and Arbitrum. The results indicate that SRVs are widespread, with affected contracts holding $4.76 million in active assets. Among these, 19.63% of contracts that use signatures on Ethereum contain SRVs. Furthermore, manual verification demonstrates that LASiR achieves an F1-score of 87.90% for detection. Ablation studies and comparative experiments reveal that the semantic information provided by LLMs aids static taint analysis, significantly enhancing LASiR's detection performance.

Sabin Roman, Francesco Bertolotti

Technological developments and the impact of artificial intelligence (AI) are omnipresent themes and concerns of the present day. Much has been written on these topics but applications of quantitative models to understand the techno-social landscape have been much more limited. We propose a mathematical model that can help understand in a unified manner the patterns underlying technological development and also identify the different regimes in which the technological landscape evolves. First, we develop a model of innovation diffusion between different technologies, the growth of each reinforcing the development of the others. The model has a variable that quantifies the level of development (or innovation, discovery) potential for a given technology. The potential, or market capacity, increases via diffusion from related technologies, reflecting the fact that a technology does not develop in isolation. Hence, the growth of each technology is influenced by how developed its neighboring (related) technologies are. This allows us to reproduce long-term trends seen in computing technology and large language models (LLMs). We then present a three-dimensional system of supply, demand, and investment which shows oscillations (business cycles) emerging if investment is too high into a given technology, product, or market. We finally combine the two models through a common variable and show that if investment or diffusion is too high in the network context, chaotic boom-bust cycles can emerge. These quantitative considerations allow us to reproduce the boom-bust patterns seen in non-fungible token (NFT) transaction data and also have deep implications for the development of AI which we highlight, such as the arrival of a new AI winter.

Chin-Yuan Yeh, Hsi-Wen Chen, De-Nian Yang, Wang-Chien Lee · 6 authors

Recently, Non-Fungible Tokens (NFTs) have attracted attention as valuable digital assets. However, NFT marketplaces face complex challenges in simultaneously recommending optimal pricing to sellers and desirable NFTs to buyers. Unlike conventional marketplaces that focus only on balancing demand and supply between sellers and buyers, these tasks are complicated by intricate value interdependencies arising from diverse buyer preferences, budgets, trait rarities, and the unprecedented breeding mechanisms. This paper formulates the NFT Project Pricing/Purchasing Recommendation (NP3R) problem, aiming to achieve a competitive equilibrium that concurrently optimizes seller revenue and buyer utility. We introduce BANTER, an iterative algorithm that jointly determines (1) optimal NFT purchases for buyers (via NFT-REC), considering breeding utility and current prices; and (2) optimal pricing for sellers (via PRICEREC), based on aggregated demand from NFT-REC. To efficiently manage the combinatorial complexity of breeding, we devise Optimal Parent Pair Selection (OPPS) and Heterogeneous Parent Set Selection (HPSS) schemes. Theoretical analysis guarantees BANTER to converge to a competitive equilibrium. Experiments on five real-world NFT datasets demonstrate its effectiveness in enhancing both seller revenue and average buyer utility. Source code: https://github.com/jimmy-academia/BANTER

Hamza Zarfaoui

Master's

Ryo Watanabe, Wutichai Chongchitmate, Nagul Cooharojananone

In admissions and hiring, transcript verification often requires only a threshold decision, for example a grade point average of 3.0 or higher or a Python grade of B or better. However, prevailing workflows demand full transcript disclosure, creating privacy risks and evaluation bias. We present a deployable system that proves a chosen subject meets a required threshold without revealing the score or any other subjects. The system integrates Groth16 Succinct Noninteractive Arguments of Knowledge (SNARKs) with W3C Decentralized Identifiers (DID) and Verifiable Credentials (VC) in a four-service architecture for DID registration, university issuance, student proving, and third-party verification. Holder identity is enforced offcircuit via a DID based challenge-response protocol where the student signs the verifier's nonce and the verifier checks that the signer's DID matches the VC's subject identifier. The zero-knowledge (ZK) circuit proves only Merkle inclusion and threshold comparison. The university signs the transcript's Merkle root with an Ed25519 signature. The verifier validates this signature using a key obtained from the university domain or a trusted registry, never from the student. On standard development hardware, steady-state proving latency is between 0.66 and 1.10 seconds. The verifier learns only a pass/fail bit, enforcing data minimization. In our negative test suite, no false accepts were observed.

Fidel T Rorimpandey, Neorafa A Zulkarnaen, Aditya Kurniawan, Chrisando Ryan Pardomuan Siahaan

This paper is focused on Proof of Authority (POA) consensus mechanism in blockchain-based e-voting for student body elections. Exploring a unique take on consensus rather than using the popular consensus like Proof of Stake or Proof of Work. POA, with its reliance on trusted validators, offers a promising solution for low-cost, secure, and transparent voting. We developed a prototype POA-based e-voting model designed for student body elections and get results of its effectiveness in terms of reliability. Our findings indicate that POA helps the security and accessibility of e-voting, making it suitable for voting validity.

Md Imran Khan, Ahmad Raza, Abdulrahman Alomair, Abdulaziz S. Al Naim

This study provides a comprehensive contribution to the current understanding of blockchain technology and non-fungible token (NFTs). Blockchain technology is a revolutionary data storage and management tool that records data shared across a network of computers globally, making it safe, transparent, and decentralized. Non-fungible token is a specific type of token built on a blockchain, enabling the authentication of digital assets and safeguarding them against copying or fabrication. The research employed information on 3760 abstract data collected for the period from January 1, 2017, to June 03, 2025. The search criteria for data retrieval are based on the following keywords: “blockchain”, “non-fungible token”, and “token”. The data sources are Scopus and Web of Science. The study highlights the multi-dimensional and evolving discussion around blockchain and NFTs, including elements of technology, security, money, digital rights, and decentralization. The Wordcloud indicates a strong and growing innovation ecosystem, proposing new study avenues in trust mechanisms, smart contract development, and tokenized economies. The correlation graph visualizes that AI, data, finance, and blockchain show their mutual dependence has revolutionized our view of autonomy and governance. The study highlights authors who actively research blockchain and NFTs, as well as correlations between them. China leads the way in this research area and USA leads in terms of citation. The study’s finding informs evidence-based decision-making regarding the regulation and governance of blockchain and NFT technologies. For industry practitioners, the study’s insights can guide the development of innovative applications and solutions leveraging blockchain and NFTs. By examining a vast dataset of academic papers, the inquiry illuminates the key themes, emerging trends, and potential research gaps within this rapidly evolving field.