Blockchain Papers

Brian Sanya Mondoh, Sara M. Johnson, Palesa Roza Gwele

No abstract is available for this record.

Wayne R. Landsman, Evgeny Lyandres, Edward L. Maydew, Daniel Rabetti · 5 authors

No abstract is available for this record.

Seongho Yoon, Jin‐Young Choi

Smart contracts provide convenience to the financial industry by automating complex transactions without intermediaries. In the context of decentralized finance, ensuring the correctness, reliability, and efficiency of these automated systems is crucial, especially for loan management processes. This paper presents a formal specification of a smart contract-based loan management system using TLA+. The system is designed to handle various types of loans, including credit-based and collateral-backed loans, managing both regular and early repayments, as well as handling late payments and collateral forfeiture. Our specification defines key components such as loan states, repayment calculations, and collateral management, ensuring that all aspects of the loan lifecycle are rigorously modeled and verified. Safety properties, such as preventing negative loan balances and ensuring proper collateral ownership transfer, are enforced alongside liveness properties that guarantee the system reaches a termination state where all loans are either fully repaid or defaulted. We used the TLC model checker to verify the correctness of the system across all possible states and transitions. The verification process confirmed that the system consistently adheres to its formal specification under a variety of operational conditions. Through the formal specification and comprehensive verification with TLA+, this work enhances the dependability of smart contract based financial systems, providing a secure, verifiable, and resilient loan management framework ready for deployment in real-world DeFi environments.

Akaash Vishal Hazarika, Mahak Shah, Swapnil Patil, Pradyumna Shukla

Effective risk management solutions become absolutely crucial when financial markets embrace distributed technology and decentralized financing (DeFi). This study offers a thorough survey and comparative analysis of the integration of artificial intelligence (AI) in risk management for distributed arbitrage systems. We examine several modern caching techniques namely in memory caching, distributed caching, and proxy caching and their functions in enhancing performance in decentralized settings. Through literature review we examine the utilization of AI techniques for alleviating risks related to market volatility, liquidity challenges, operational failures, regulatory compliance, and security threats. This comparison research evaluates various case studies from prominent DeFi technologies, emphasizing critical performance metrics like latency reduction, load balancing, and system resilience. Additionally, we examine the problems and trade offs associated with these technologies, emphasizing their effects on consistency, scalability, and fault tolerance. By meticulously analyzing real world applications, specifically centering on the Aave platform as our principal case study, we illustrate how the purposeful amalgamation of AI with contemporary caching methodologies has revolutionized risk management in distributed arbitrage systems.

Eashwar Sivakumar, Kiran Jot Singh, Paras Chawla, G. Geetha

The complex nature of smart contracts necessities the development of a novel adaptable framework. As blockchain technology continues to expand into diverse fields, the demand for secure, efficient, and transparent systems becomes increasingly critical. RBEDH is the integration of Role Based Access Control (RBAC), Event Driven Architecture (EDA) and Hybrid Functionalities. The framework is applied to the scholarly publishing sector, where academic integrity, transparency, and reliability are crucial for ensuring the credibility and trustworthiness of published research. The smart contracts are executed between authors, reviewers and publishers. Functionality test was conducted using Ganache, Ropsten, and Ethereum Mainnet. These evaluations confirmed the consistency and effectiveness of contract deployment. Vulnerabilities such as re-entrancy, integer overflow/underflow, unauthorized access were tested using Securify, Mythril, Smartcheck and Oynete. It is found that the system is secure and it not susceptible to any of the above vulnerability. Further timestamp dependency was tested through Manticore, Slither and Echidna and the test results indicate the absence of vulnerability. Scalability and performance analysis results proves that the proposed framework is better on the basis of average energy consumption, latency and memory requirement when compared with the existing literature.

Liang Guo

Blockchain technology can generate decentralized consensus , thereby enhancing the verifiability and contractability of payoff-relevant states in a distribution channel or supply chain (e.g., seller cost and/or buyer value). Therefore, smart contracts can be written between supply chain partners such that transactions are automatically executed at state-dependent prices. The author examines the economic impacts of blockchain-enabled smart contracts on the equilibrium generation and allocation of surplus in a three-level supply chain. The model highlights the role of smart contracts in removing ex post asymmetric decision rights, which are otherwise inherently present under regular contracts where trade decisions are made based on constant transfer prices. As a result, the seller (under cost uncertainty) or the buyer (under value uncertainty) may hurt itself by signing a smart contract with the middleman, despite the improvement in the supply chain's total efficiency and surplus. Therefore, the equilibrium choice of a smart contract can be socially insufficient. Moreover, interestingly, a prisoner's dilemma may arise: The seller's and the buyer's dominant choice is the regular contract, whereas everyone would be better off if the smart contract is jointly adopted by all parties.

Rim Ben Fekih, Mariam Lahami, Salma Bradai, Mohamed Jmaïel

Defined as an agreement between multiple parties and systematically executed by a computer code, smart contracts enable trust-less execution without a third party. Despite the trusted implementations that smart contracts offer, including those based on standards, different security problems and vulnerabilities arise during their development and execution. To address these issues, multiple studies have proposed potential solutions, focusing particularly on the verification of smart contracts and considering the standard-based ones using formal verification techniques. However, the sheer amount of research makes it difficult to accurately articulate the state-of-the-art. To tackle this challenge, we propose a systematic literature review that deals with formal verification of ERC-based smart contracts. ERC (Ethereum Request for Comments) standards enable a range of functionalities, such as the creation and management of tokens. Thus, our review provides an overview of ERC standards and examines their related potential issues. Furthermore, we investigate existing solutions presented in 19 relevant studies published between 2019 and July 2023. We analyze and classify approaches to formal modeling, properties’ specification and techniques used in the verification of smart contracts. Finally, we discuss the research challenges and suggest some promising future directions to stir research efforts into this area.

kaushal patidar

No abstract is available for this record.

Michael Bresil, Pwc Prasad, Md Shohel Sayeed, Umar Ali Bukar

In the short history of smart contracts, substantial losses have occurred due to unaccounted vulnerabilities in the smart contracts loaded onto the blockchain. Vulnerabilities in smart contracts threaten the viability and confidence of blockchain technology. Machine and deep learning architectures have been increasingly proposed to assist with writing smart contracts and detecting vulnerabilities to minimize the risks of successful attacks. This paper presents the findings of deep learning vulnerability detection in smart contracts collated from selected research papers and provides an overview of the vulnerability detection architecture. This paper assessed each component of the overall architecture, individually and in combinations, to infer potential relationships in increasing detection rates in smart contract vulnerability detection tools. This study found that the area of detecting smart contract vulnerabilities is gravitating towards deep learning models, with a particular focus on combining neural networks in serial or parallel to achieve high detection results. Feature extraction of syntax and semantic information greatly determines the detection results of a model, which suggests a strong relationship between the use of source code and neural networks. Despite this relationship, given the inaccessibility and unavailability of smart contract source code, future work should focus on efficiently extracting features and context from opcodes, which are more readily available on the blockchain. Based on these findings, the study offers an in-depth discussion of the matters arising, open issues, and key aspects such as bytecode and opcode in smart contracts, failure of attention mechanisms, the rationale of hybrid models, dataset challenges, computational complexity in a large-scale blockchain environment, optimization strategies, Expunge and interdisciplinary approaches to smart contract vulnerability detection. These discussions enhance the practical relevance of the paper, highlighting the opportunities for scaling deep learning models for real-world blockchain applications.

Tao Yan, Y. Han Kim, Shengnan Li, Taehoon Kim · 5 authors

No abstract is available for this record.

Qiyang Song, Heqing Huang, Xiaoqi Jia, Yuanbo Xie · 5 authors

Reentrancy vulnerabilities in Ethereum smart contracts have caused significant financial losses, prompting the creation of several automated reentrancy detectors.However, these detectors frequently yield a high rate of false positives due to coarse detection rules, often misclassifying contracts protected by anti-reentrancy patterns as vulnerable.Thus, there is a critical need for the development of specialized automated tools to assist these detectors in accurately identifying anti-reentrancy patterns.While existing code analysis techniques show promise for this specific task, they still face significant challenges in recognizing anti-reentrancy patterns.These challenges are primarily due to the complex and varied features of anti-reentrancy patterns, compounded by insufficient prior knowledge about these features.This paper introduces AutoAR, an automated recognition system designed to explore and identify prevalent anti-reentrancy patterns in Ethereum contracts.AutoAR utilizes a specialized graph representation, RentPDG, combined with a data filtration approach, to effectively capture anti-reentrancy-related semantics from a large pool of contracts.Based on RentPDGs extracted from these contracts, AutoAR employs a recognition model that integrates a graph auto-encoder with a clustering technique, specifically tailored for precise anti-reentrancy pattern identification.Experimental results show AutoAR can assist existing detectors in identifying 12 prevalent anti-reentrancy patterns with 89% accuracy, and when integrated into the detection workflow, it significantly reduces false positives by over 85%.* The corresponding authors.a reentrancy vulnerability in Ethereum, leading to substantial financial losses.

Saeid Ataei, Saeid Ataei, Shervan Ataei, Shervan Ataei · 5 authors

This study presents a comprehensive systematic review of Artificial Intelligence (AI) applications in DecentralizedFinance (DeFi), emphasizing AI’s pivotal role in mitigating the vulnerabilities and operational complexities inherentin permissionless financial systems. By systematically analyzing 39 peer-reviewed studies from major scholarlydatabases, the review identifies five dominant application domains: fraud detection, smart contract security, marketprediction, credit risk assessment, and decentralized governance. It examines the diverse range of AI methodsspanning machine learning, deep learning, graph neural networks, and reinforcement learning—and evaluates theircomparative performance and limitations. The findings reveal that AI not only enhances DeFi’s transparency, trust,and efficiency but also underpins emerging capabilities such as autonomous governance and adaptive marketmechanisms. Persistent challenges including data scarcity, cross-chain generalization, interpretability, andscalability—underscore the need for robust, explainable, and ethical AI solutions. The review concludes that AIconstitutes a foundational enabler for secure, transparent, and resilient decentralized financial ecosystems, andoutlines critical future research directions for integrating trustworthy intelligence into the evolving DeFi landscape. (PDF) A Systematic Review on the Application of Artificial Intelligence in Decentralized Finance. Available from: https://www.researchgate.net/publication/397514996_A_Systematic_Review_on_the_Application_of_Artificial_Intelligence_in_Decentralized_Finance [accessed Nov 11 2025].

Anthony Chidi Nzomiwu, Michael Ndubisi Nwobodo

No abstract is available for this record.

Francesca Carapella, Edward J. Dumas, Jacob Gerszten, Nathan Swem · 5 authors

No abstract is available for this record.

Asad Ul Islam Khan, Rasim Özcan, Mohamed Abbas Ibrahim

In this paper, we use the Empirical Bayes estimation and multiple linear regression approach to examine the impact of the top 5 cryptocurrencies’ crash risks on the G-7 and China equity markets’ crash risks. MATLAB was used to calculate the crash risks, while Stata software was employed for the econometric analysis. Three crash risk measures are usedto validate the robustness of the results: (i) the relative frequency of the number of crash days in the market, (ii) the monthly returns’ skewness, and (iii) the down-to-up volatility. Our findings indicate that overall crash risks of the top 5 cryptocurrencies are positively related with G-7 and Chinese stock markets’ crash risk. This suggests that the crash risk transmits from the crypto to the equity markets and the crashes in crypto can serve as a predictor in the stock markets. Furthermore, there is a negative correlation between the historical crash risks of the G-7 stock market and the present crash risks of the same stock market. This suggests that past stock market crashes can serve as a predictive factor for assessing the current risk of a stock market crash.

Oluwasanmi Segun Adanigbo, Florence Sophia Ezeh, Unomah Success Ugbaja, Comfort Iyabode Lawal · 5 authors

This paper explores the convergence of Blockchain and Internet of Things (IoT) technologies and their transformative potential in digital financial transactions. With the rise of decentralized systems, blockchain provides a secure, transparent, and immutable ledger for recording financial transactions, while IoT enables real-time data collection and automation in financial networks. The integration of these two technologies offers enhanced scalability, security, and efficiency, significantly improving the performance of digital financial systems. However, the combination of blockchain and IoT introduces new challenges, including security vulnerabilities, privacy concerns, and the need for effective mitigation strategies. The paper highlights key trends, such as the rise of smart contracts, tokenization, decentralized finance (DeFi) applications, and the incorporation of artificial intelligence (AI) and machine learning (ML) to optimize blockchain-IoT ecosystems. It also examines the evolving regulatory and ethical considerations for these hybrid systems. As these technologies continue to mature, understanding their potential and addressing their associated risks will be critical for the future of digital financial transactions.

Ignat Melnikov, Irina Lebedeva, Artem Petrov, Yury Yanovich

Decentralized finance (DeFi) is a rapidly evolving blockchain technology that offers a new perspective on financial services through Web3 applications. DeFi offers developers the flexibility to create financial services using smart contracts, leading to a lack of standardized protocols and challenges in applying traditional finance models for risk assessment, especially in the early stages of adoption. The Maker protocol is a prominent DeFi platform known for its diverse functionalities, including loan services. This study focuses on analyzing the risk associated with Maker's loan portfolio by developing a risk model based on multiple Brownian motions and passage levels, with Brownian motions representing different collateral types and passage levels representing users' collateralization ratios. Through numerical experiments using artificial and real data, we evaluate the model's effectiveness in assessing risk within the loan portfolio. While our findings demonstrate the model's potential for assessing risk within a single DeFi project, it is important to acknowledge that the model's assumptions may not be fully applicable to real-world data. This research underscores the importance of developing project-specific risk assessment models for individual DeFi projects and encourages further exploration of other DeFi protocols.

Mihai Andronie, Roman Blažek, Mariana Iatagan, Renata Skýpalová · 16 authors

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

Fatou Ndiaye Mbodji, Boladji Vinny Adjibi, M. Diouf, Gervais Mendy · 7 authors

Due to the inherent immutability of blockchain technology, smart contract updates require their deployment at new addresses rather than modifying existing ones, thus fragmenting version histories and creating critical blind spots for analyses. Indeed, for example, this fragmentation severely hinders security researchers ability to track vulnerability lifecycles across contract versions. While platforms like Etherscan provide detailed information about Ethereum smart contracts, they lack crucial functionality to trace predecessor-successor relationships within smart contract lineages, preventing systematic analysis of how vulnerabilities emerge, propagate, and potentially remain unresolved across versions.To address the challenge of tracing smart contract lineages, we adopt a Design Science Research (DSR) approach and introduce ContractTrace, an automated infrastructure that accurately identifies and links versions of smart contracts into coherent lineages. This tool enables the construction of lineageSet, an up-to-date, open-source dataset specifically designed to support security research on vulnerability, defect or any other property evolution patterns in smart contracts. Through a security-focused case study we demonstrate how ContractTrace reveals previously obscured vulnerability life-cycles within smart contract lineages, tracking whether critical security flaws persist or get resolved across versions. This capability is essential for understanding vulnerability propagation patterns and evaluating the effectiveness of security patches in blockchain environments. In the evaluation phase of our DSR approach, we validated our lineage detection methodology against an alternative approach using Locality-Sensitive Hashing (LSH) to cluster contract versions, confirming the security relevance and accuracy of our technique.

Sepideh HajiHosseinKhani, Arash Habibi Lashkari, Ali Mizani Oskui

With the advent of blockchain networks, there has been a transition from traditional contracts to Smart Contracts (SCs), which are crucial for maintaining trust within these networks. Previous methods for analyzing SCs vulnerabilities typically suffer from a lack of accuracy and effectiveness. Many of them, such as rule-based methods, machine learning techniques , and neural networks , also struggle to detect complex vulnerabilities due to limited data availability. This study introduces a novel approach to detecting, identifying, and profiling SC vulnerabilities, comprising two key components: an updated analyzer named SCsVulLyzer (V2.0) and an advanced Genetic Algorithm (GA) profiling method. The analyzer extracts 240 features across different categories, while the enhanced GA, explicitly designed for profiling SC vulnerabilities, employs techniques such as penalty fitness function, retention of elites, and adaptive mutation rate to create a detailed profile for each vulnerability. Furthermore, due to the lack of comprehensive validation and evaluation datasets with sufficient samples and diverse vulnerabilities, this work introduces a new dataset named BCCC-SCsVul-2024. This dataset consists of 111,897 Solidity source code samples, ensuring the practical validation of the proposed approach. Additionally, three types of taxonomies are established, covering SC literature review, profiling techniques, and feature extraction. These taxonomies offer a systematic classification and analysis of information, enhancing the efficiency of the proposed profiling technique. Our proposed approach demonstrated superior capabilities with higher precision and accuracy through rigorous testing and experimentation. It not only showed excellent results for evaluation parameters but also proved highly efficient in terms of time and space complexity. Moreover, the concept of the profiling technique makes our model highly transparent and explainable. These promising results highlight the potential of GA-based profiling to improve the detection and identification of SC vulnerabilities, contributing to enhanced security in blockchain networks.

Piotr Manikowski, Weronika Szymczak-Łączna, Bojan Srbinoski

This article concerns the possibilities of insuring cryptocurrency wallets using various assumptions and characteristics of perfectly insurable risk. The main goal of this article is to examine if and how cryptocurrency wallets risk fulfils the requirements of an ideally insurable risk. The topic of the research is important looking at the newest trends in financial markets and the growing number of cryptocurrency investors. The paper presents authors’ innovative method of approaching a part of cryptocurrency risk to insurance industry. Authors analysed requirements of an insurable risk and applied these requirements to a specific risk, i.e. the cryptocurrency wallet risk to further check if it is possible to insure such a risk. By introducing and defining cryptocurrency wallet risk, we found an element of cryptocurrencies which shows traits of a non-speculative risk and possibly fulfils insurability characteristics.

Bhalchandra M Hardas

Blockchain technology has emerged as a pivotal solution for enhancing security in financial transactions through its decentralized architecture. Traditional financial systems are often vulnerable to fraud, data breaches, and single points of failure due to centralization. Blockchain, with its distributed ledger system, ensures transparency, immutability, and security by verifying transactions across a network of nodes without relying on a central authority. This decentralized approach mitigates the risks associated with centralized databases, such as hacking or unauthorized access. Blockchain’s cryptographic algorithms safeguard transaction integrity and protect sensitive financial information. Smart contracts automate processes, reducing human error and the need for intermediaries, thereby increasing efficiency while lowering operational costs. Despite its benefits, challenges remain, including scalability issues, regulatory hurdles, and energy consumption. As blockchain technology continues to evolve, it holds the potential to revolutionize financial transactions, offering a robust, secure, and transparent framework for future financial systems. This paper explores the mechanisms behind blockchain security and its impact on modern financial infrastructures.

Chavhan Sujeet Yashavant, MitrajSinh Chavda, Saurabh Kumar, Amey Karkare · 5 authors

Smart Contracts (SCs) handle transactions in the Ethereum blockchain worth\nmillions of United States dollars, making them a lucrative target for attackers\nseeking to exploit vulnerabilities and steal funds. The Ethereum community has\ndeveloped a rich set of tools to detect vulnerabilities in SCs, including\nreentrancy (RE) and unhandled exceptions (UX). A dataset of SCs labelled with\nvulnerabilities is needed to evaluate the tools' efficacy. Existing SC datasets\nwith labelled vulnerabilities have limitations, such as covering only a limited\nrange of vulnerability scenarios and containing incorrect labels. As a result,\nthere is a lack of a standardized dataset to compare the performances of these\ntools. SCRUBD aims to fill this gap. We present a dataset of real-world SCs and\nsynthesized SCs labelled with RE and UX. The real-world SC dataset is labelled\nthrough crowdsourcing, followed by manual inspection by an expert, and covers\nboth RE and UX vulnerabilities. On the other hand, the synthesized dataset is\ncarefully crafted to cover various RE scenarios only. Using SCRUBD we compared\nthe performance of six popular vulnerability detection tools. Based on our\nstudy, we found that Slither outperforms other tools on a crowdsourced dataset\nin detecting RE vulnerabilities, while Sailfish outperforms other tools on a\nmanually synthesized dataset for detecting RE. For UX vulnerabilities, Slither\noutperforms all other tools.\n

Ahsan Adeleke Akoshile, Olamide Jogunola, Mohammad Hammoudeh, Tooska Dargahi

Recent research has exposed significant security vulnerabilities within smart contracts that run on blockchain.Threats, such as, reentrancy attacks, where malicious actors exploit recursive function calls in a smart contract, pose a critical threat.This led to substantial financial losses in organisations.Traditional vulnerability detection methods, largely based on static analysis, showed limitations in effectively identifying reentrancy issues, often yielding high false positive rates and missing complex execution paths.This paper analyses hybrid deep learning models for reentrancy vulnerability detection in Ethereum smart contracts, introducing a unique approach that combines semantic and syntactic feature extraction.Specifically, our approach integrates CodeBERT embeddings for deep semantic insights with pattern-based feature vectors that capture Solidity constructs that are vulnerable to reentrancy attacks.Five hybrid models are evaluated, each selected to provide insights into structural and sequential dependencies within code.Findings highlighted the novelty of using multimodal feature integration in vulnerability detection, with models like Autoencoder-LSTM and CodeBERT-Transformer Encoder achieving high accuracy of 98.3% and 98.01%, respectively, demonstrating the effectiveness of hybrid architectures for capturing complex vulnerability patterns.This comparative study advances the smart contract security field, showcasing each model's strengths and trade-offs, and providing practical guidance for deploying deep learning-based vulnerability detection within blockchain ecosystems.