Security Of Smart Contracts Research Articles

Tokenization of Agricultural Assets: Strengthening Blockchain Security in Agri-finance and Investment Models Against Fraud and Cyber Risks

Blockchain-based tokenization is transforming agricultural finance by enhancing efficiency, transparency, and security. This study focuses on small farmers and agribusinesses, evaluating the role of blockchain in mitigating fraud and cyber risks while improving financial accessibility. Using datasets from the World Bank Global Findex (measuring financial inclusion in agriculture), Ethereum blockchain transaction records (analyzing tokenized asset security and fraud detection), and FAO Agristats (assessing financial efficiency in agricultural markets), the study provides an empirical assessment of tokenization's impact. Methodologically, logistic regression is employed to predict fraud risk based on financial accessibility and regulatory strength, while anomaly detection using Z-scores identifies irregular blockchain transactions. Additionally, a Difference-in-Differences (DiD) regression model evaluates financial efficiency improvements, measuring the impact of tokenization on loan disbursement times, transaction costs, and transparency. The findings indicate that tokenization reduces loan disbursement times by 31.3%, lowers transaction costs by 37.3%, and increases financial transparency by 40%. Fraud occurrences were 15% lower in blockchain-enabled transactions, with a weak correlation (r = -0.064) between digital finance adoption and fraud rates, suggesting that governance weaknesses rather than digitalization alone influence fraud risks. The study recommends advanced smart contract security, decentralized identity verification, regulatory oversight, and oracle network integration to enhance blockchain security in agricultural finance. These insights underscore the importance of multi-layered cybersecurity strategies in securing tokenized agricultural investments, benefiting farmers, agribusinesses, and financial institutions while fostering a more transparent and efficient agri-finance ecosystem.

Secure blockchain-based electronic toll collection system: enhancing location verification and smart contract resilience

ABSTRACT Electronic Toll Collection (ETC) systems aim to replace the traditional toll and lies in enhancing traffic flow efficiency. However current ETC systems are not secure to preserve privacy for vehicle’s information and help against theft of funds. This makes automatic payment not efficient and vulnerable to attacks (i.e. Reentrancy attack). To address these challenges, our paper proposes leveraging Ethereum blockchain and smart contracts for automatic payment mechanisms in ETC systems. The primary challenges include authenticating vehicle data, automatically deducting fees, and safeguarding against reentrancy attacks on smart contracts while maintaining the confidentiality of distance-related information. To tackle these issues, this paper introduces a novel decentralized application (DApp), called DAppETC. This solution employs a private blockchain with end-to-end verification algorithms at toll points. These algorithms ensure both the protection of sensitive distance information and the enhancement of smart contract security against reentrancy attacks. The performance evaluation of the proposed system is carried out on a private blockchain. To assess the robustness of our approach, we introduce a hacker smart contract to evaluate the effectiveness of the deployed end-to-end verification algorithms at both entry and exit toll points.

Blockchain and Cybersecurity: A dual approach to securing financial transactions in Fintech

The rapid growth of the fintech industry has led to an increased reliance on digital financial transactions, which simultaneously presents significant cybersecurity risks. This abstract explores the potential of combining blockchain technology with advanced cybersecurity protocols to create a dual-layered approach to securing financial transactions and protecting customer data in fintech applications. Blockchain, with its decentralized and immutable nature, offers inherent security benefits, making it an ideal foundation for enhancing the integrity of financial transactions. Each transaction recorded on a blockchain is cryptographically secured, ensuring that data cannot be altered retroactively, thus preventing fraud and unauthorized access. However, while blockchain technology provides a robust mechanism for securing transaction data, it is not immune to all forms of cyber threats, particularly those targeting the endpoints or vulnerabilities in smart contracts. Advanced cybersecurity protocols, such as encryption, multi-factor authentication, and intrusion detection systems, complement blockchain’s capabilities by safeguarding the broader fintech ecosystem. These protocols can be used to secure access points, ensure data privacy, and enhance the detection of malicious activities that might bypass blockchain’s security features. The integration of blockchain with cybersecurity measures creates a multi-layered defense system that not only protects transaction integrity but also mitigates risks associated with data breaches, phishing attacks, and insider threats. For example, encryption techniques can ensure that sensitive customer data remains confidential, while blockchain ensures the immutability of transaction records. Furthermore, smart contract security protocols can prevent vulnerabilities in decentralized finance (DeFi) applications from being exploited by malicious actors. By combining these two technologies, fintech companies can significantly enhance the security of financial transactions, improve customer trust, and ensure compliance with regulatory standards. This dual approach strengthens the resilience of fintech platforms against evolving cyber threats and enhances the overall security posture of the industry. Keywords: Blockchain, Cybersecurity, Fintech, Financial Transactions, Data Integrity, Encryption, Smart Contracts, Decentralized Finance, Multi-Layered Security, Fraud Prevention.

Towards a Secure Blockchain Ecosystem: Current Vulnerabilities and Future Directions in Smart Contract Security

As a distributed shared transaction ledger, blockchain technology has the characteristics of decentralization, immutable, irreversible and traceable, and is changing the inherent model of traditional industries. Smart contracts, as one of the core applications of blockchain technology, provide the basis for a variety of practical applications. However, the frequent security problems of smart contracts have not only caused huge economic losses, but also hindered the development of blockchain systems. According to relevant studies, the economic losses caused by smart contract security breaches have exceeded billions of dollars. Therefore, the security of smart contracts has become a hot topic at home and abroad. This study discusses the technical vulnerabilities and corresponding solutions of smart contracts from the code level. In-depth analysis of security vulnerabilities in smart contracts can help identify and repair potential security hazards, improve the overall security of contracts, and prevent the theft of funds or abnormal execution of contracts. By raising the security awareness of developers, enterprises and users, it is possible to promote the application of smart contracts in high-security fields such as finance and law and promote the healthy development of the blockchain ecosystem.

Блокчейн як інструмент зміцнення фінансової безпеки держави

Traditional financial services are characterized by high complexity, multi-level structures, and dependence on intermediaries, which create significant operational costs, lengthy settlement times, and system risks. The article analyzes modern financial systems such as SWIFT, clearinghouses, central securities depositories, and central counter-parties, highlighting the key drawbacks of a centralized approach: data fragmentation, the need for synchronization, failure risks, and high compliance costs. The article also addresses contemporary challenges, including slow settlement times (for example, T+2 for equities), reliance on correspondent banking networks, and costs associated with risk management. The aim of the article is to survey the impact of blockchain technologies on the financial sector, assessing the opportunities and challenges of implementing blockchain to optimize financial processes. The article compares traditional financial systems with innovative approaches based on distributed ledgers, such as blockchain, in terms of performance, transparency, security, regulatory compliance, and cost. The research methodology includes a review of the relevant literature, an overview of existing platforms (Ethereum, Hyperledger Fabric, R3 Corda, Quorum), and their applications in the financial sector. The article examines the technological aspects of blockchain, including distributed ledgers, consensus algorithms, smart contracts, and asset tokenization. The advantages of blockchain technology are identified, particularly the automation of processes, reduction of reliance on intermediaries, increased transparency, and shortened settlement times. The prospects of decentralized finance (DeFi) and corporate blockchain solutions are analyzed, particularly the use of smart contracts and tokenization to enhance liquidity. The research results indicate that the implementation of blockchain can significantly reduce operational costs, enhance transaction transparency, and ensure the speed of financial settlements. In particular, blockchain shortens the T+2 settlement cycle to seconds, improving liquidity and efficiency in financial markets. The conclusion of the article offers recommendations for selecting blockchain platforms based on the needs of financial institutions. Key criteria such as confidentiality, scalability, transaction throughput, and compliance with regulatory requirements are discussed. The necessity of creating a unified regulatory framework to support the implementation of blockchain in finance is particularly emphasized. Future research prospects include the development of interoperability tools, enhancing the security of smart contracts, and long-term evaluation of the efficiency of blockchain solutions in production environments. Thus, blockchain is an important tool for the transformation of financial systems, ensuring a significant increase in their efficiency, resilience, and transparency.

Blockchain Technology in Supply Chain Management: Developing Smart Contracts Based on Python

With the development of the global economy and the progress of science and technology, the importance of supply chain management in modern economic activities has become increasingly prominent. However, traditional supply chain management is characterized by problems such as information silos, low transparency, and difficulties in traceability, which seriously affect the efficiency and security of the supply chain. The introduction of blockchain technology provides a new technical path and solution to solve these problems. This paper explores the application of blockchain technology, especially smart contracts, in supply chain management. By developing Python-based smart contracts, this paper verifies the effectiveness of blockchain technology in enhancing supply chain transparency, efficiency, security, and trust. In addition, this paper demonstrates the current status and challenges of the application of blockchain technology in supply chain management through literature review and case study analysis, and points out the direction of future research. The findings of this paper show that the application of blockchain technology in supply chain management has significant advantages, but it is still necessary to further optimize the performance and security of smart contracts and explore more application scenarios.

Una aproximación al uso de patrones de eventos para el modelado de contratos inteligentes

In this work, it is proposed to develop a smart contract design model event patterns-based. The use of event patterns in smart contract modeling is an emerging trend. This systematized and agile process allows, through event patterns, to reuse the knowledge and experience of previous solutions described with business patterns for the same use cases. Smart contract is programming code that is automatically executed when certain predefined conditions are met. These contracts are created and stored on a Blockchain platform, allowing for automatic execution and verification by multiple nodes on the network. Mapping the scientific literature published to date, one finds works that propose the application of patterns to the modeling of smart contracts. The results show that the use of event patterns in smart contract modeling is an emerging trend. Event patterns are used to model interactions between smart contracts and their environment, helping to improve the efficiency, security, and flexibility of smart contracts. Among the most commonly used event patterns are time patterns, flow control patterns, and communication patterns. In addition, specific tools and languages have been developed for modeling smart contracts based on event patterns. In conclusion, it is suggested that the use of event patterns in smart contract modeling is a promising trend that can significantly improve the efficiency, security and flexibility of smart contracts in various applications.

Belief game: Verifying smart contract functionality in player dynamic interactions

Belief game: Verifying smart contract functionality in player dynamic interactions

Automatic construction and verification algorithm for smart contracts based on formal verification

As an emerging technology, blockchain demonstrates strong potential for applications in digital finance. As a core component of blockchain, the security and reliability of smart contracts is crucial. To ensure the high reliability of smart contracts, this study employs formal construction and verification techniques based on game theory. Initially, the profit function is defined using distortion techniques, and a game model for supply chain participation is designed. However, the equilibrium solution of the two-party game does not represent the optimal solution for the supply chain system. Therefore, the study introduces third-party participation to optimize the equilibrium solution. Finally, a probability model detection method is used to verify the constructed smart contract model. The results show that the supply chain model, analyzed through formal methods, has attributes consistent with theoretical analysis. Consequently, the research on automatic construction and verification algorithms for smart contracts based on formal verification proves to be effective and feasible in practical applications.

Advanced Security Auditing Methods for Solidity-Based Smart Contracts

The development of smart contracts remains in its early stages, with significant differences in underlying programming languages and application platforms resulting in a lack of standardization. This lack of standardization increases the susceptibility to vulnerabilities and associated financial losses. To address security vulnerabilities in smart contracts on the Ethereum blockchain platform, this paper proposes a security audit method based on formal verification. The method integrates an input module, static analysis module, formal verification module, analog execution module, and report and recommendation module, which can accurately discover the security vulnerabilities and logical flaws of smart contracts through formal verification and other analysis techniques, thus realizing correctness detection. During the experiment, the method detects 8 types of common vulnerabilities in 148 smart contracts and marks 21 smart contracts with vulnerabilities. After manual review and analysis, it is found that 17 of these 21 marked smart contracts do have security vulnerabilities. The experimental results show that the proposed method can accurately detect security vulnerabilities and logic flaws in smart contracts through formal verification and other analysis techniques before smart contracts are deployed, thus significantly improving the security of smart contracts and reducing the economic losses that may be caused by code defects.

Integration of NLP and machine learning methods for smart contract security: a comparison with traditional approaches

In modern blockchain systems, smart contracts are one of the most critical components for ensuring the automated execution of agreements without the need for intermediaries. However, smart contracts written in languages like Solidity may contain vulnerabilities that can be exploited by malicious actors to steal funds or manipulate assets. Given the increasing number of attacks on smart contracts, the development of effective methods for detecting such vulnerabilities is crucial. Traditional approaches to detecting vulnerabilities in smart contracts include symbolic execution, fuzzing, formal verification, and pattern matching. These methods have their advantages but face several challenges, such as high resource consumption, limitations in detecting new types of vulnerabilities, and difficulties in scaling to large contracts. As a result, there is a need to introduce new approaches, such as natural language processing (NLP) and machine learning, which can address these challenges more effectively. In this study, an NLP-based method was explored, using Word2Vec to convert smart contract code into vector representations, allowing for better analysis of the semantic relationships between elements of the code. These vector representations are then fed into a bidirectional recurrent neural network with GRU blocks and an attention mechanism. This approach allows the model to focus on the most important parts of the code and improve the accuracy of vulnerability detection. The comparative analysis showed that NLP-based methods significantly outperform traditional approaches in all key metrics. In particular, the GRU model with an attention mechanism demonstrated high results in accuracy, recall, and F-measure, making it effective for detecting complex vulnerabilities such as reentrancy. Furthermore, the NLP-based approach is capable of adapting to new types of attacks thanks to training on large datasets. Thus, the integration of NLP and machine learning represents a promising direction for enhancing the security of smart contracts. Future research can focus on improving these approaches, particularly through the implementation of advanced models such as transformers.

Survey on Quality Assurance of Smart Contracts

As blockchain technology continues to advance, the secure deployment of smart contracts has become increasingly prevalent, underscoring the critical need for robust security measures. This surge in usage has led to a rise in security breaches, often resulting in substantial financial losses for users. This paper presents a comprehensive survey of smart contract quality assurance, from understanding vulnerabilities to evaluating the effectiveness of detection tools. Our work is notable for its innovative classification of forty smart contract vulnerabilities, mapping them to established attack patterns. We further examine nine defense mechanisms, assessing their efficacy in mitigating smart contract attacks. Furthermore, we develop a labeled dataset as a benchmark encompassing ten common vulnerability types, which serves as a critical resource for future research. We also conduct comprehensive experiments to evaluate fourteen vulnerability detection tools, providing a comparative analysis that highlights their strengths and limitations. In summary, this survey synthesizes state-of-the-art knowledge in smart contract security, offering practical recommendations to guide future research and foster the development of robust security practices in the field.

Software Weakness Detection in Solidity Smart Contracts Using Control and Data Flow Analysis: A Novel Approach with Graph Neural Networks

Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution.

Sligpt: A Large Language Model-Based Approach for Data Dependency Analysis on Solidity Smart Contracts

The advent of blockchain technology has revolutionized various sectors by providing transparency, immutability, and automation. Central to this revolution are smart contracts, which facilitate trustless and automated transactions across diverse domains. However, the proliferation of smart contracts has exposed significant security vulnerabilities, necessitating advanced analysis techniques. Data dependency analysis is a critical program analysis method used to enhance the testing and security of smart contracts. This paper introduces Sligpt, an innovative methodology that integrates a large language model (LLM), specifically GPT-4o, with the static analysis tool Slither, to perform data dependency analyses on Solidity smart contracts. Our approach leverages both the advanced code comprehension capabilities of GPT-4o and the advantages of a traditional analysis tool. We empirically evaluate Sligpt using a curated dataset of Ethereum smart contracts. Sligpt achieves significant improvements in precision, recall, and overall analysis depth compared with Slither and GPT-4o, providing a robust solution for data dependency analysis. This paper also discusses the challenges encountered, such as the computational resource requirements and the inherent variability in LLM outputs, while proposing future research directions to further enhance the methodology. Sligpt represents a significant advancement in the field of static analysis on smart contracts, offering a practical framework for integrating LLMs with static analysis tools.

A Dynamic Behavior Verification Method for Composite Smart Contracts Based on Model Checking

A composite smart contract can execute smart contracts that may belong to other owners or companies through external calls, bringing more security challenges to blockchain applications. Traditional static verification methods are inadequate for analyzing the dynamic execution of these contracts, resulting in misjudgment and omission issues. Therefore, this paper proposes a model checking approach based on dynamic behavior that verifies the security and business logic of composite smart contracts. Utilizing automata, the method models contracts, users, attackers, and extracts properties, focusing on six types of common security vulnerabilities. A thorough case study and experimental evaluation demonstrate the method’s efficiency in identifying vulnerabilities and ensuring alignment with business requirements. The UPPAAL tool is employed for comprehensive verification, proving its effectiveness in enhancing smart contract security.

SGDL: Smart contract vulnerability generation via deep learning

AbstractThe growing popularity of smart contracts in various areas, such as digital payments and the Internet of Things, has led to an increase in smart contract security challenges. Researchers have responded by developing vulnerability detection tools. However, the effectiveness of these tools is limited due to the lack of authentic smart contract vulnerability datasets to comprehensively assess their capacity for diverse vulnerabilities. This paper proposes a Deep Learning‐based Smart contract vulnerability Generation approach (SGDL) to overcome this challenge. SGDL utilizes static analysis techniques to extract both syntactic and semantic information from the contracts. It then uses a classification technique to match injected vulnerabilities with contracts. A generative adversarial network is employed to generate smart contract vulnerability fragments, creating a diverse and authentic pool of fragments. The vulnerability fragments are then injected into the smart contracts using an abstract syntax tree to ensure their syntactic correctness. Our experimental results demonstrate that our method is more effective than existing vulnerability injection methods in evaluating the contract vulnerability detection capacity of existing detection tools. Overall, SGDL provides a comprehensive and innovative solution to address the critical issue of authentic and diverse smart contract vulnerability datasets.

Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts

Reentrancy vulnerability as one of the most notorious vulnerabilities, has been a prominent topic in smart contract security research. Research shows that existing vulnerability detection presents a range of challenges, especially as smart contracts continue to increase in complexity. Existing tools perform poorly in terms of efficiency and successful detection rates for vulnerabilities in complex contracts. To effectively detect reentrancy vulnerabilities in contracts with complex logic, we propose a tool named SliSE. SliSE’s detection process consists of two stages: Warning Search and Symbolic Execution Verification . In Stage 1, SliSE utilizes program slicing to analyze the Inter-contract Program Dependency Graph (I-PDG) of the contract, and collects suspicious vulnerability information as warnings. In Stage 2, symbolic execution is employed to verify the reachability of these warnings, thereby enhancing vulnerability detection accuracy. SliSE obtained the best performance compared with eight state-of-the-art detection tools. It achieved an F1 score of 78.65%, surpassing the highest score recorded by an existing tool of 9.26%. Additionally, it attained a recall rate exceeding 90% for detection of contracts on Ethereum. Overall, SliSE provides a robust and efficient method for detection of Reentrancy vulnerabilities for complex contracts.

Smart contract vulnerability detection using wide and deep neural network

Smart contract vulnerability detection using wide and deep neural network

Enhancing Oyente: four new vulnerability detections for improved smart contract security analysis

Enhancing Oyente: four new vulnerability detections for improved smart contract security analysis

NLP-BERT AND OPTIMIZATION OF EFFICIENCY-SECURITY IN BLOCKCHAIN-ACCOUNTING SMART CONTRACTS

In the evolving digital era, blockchain-based accounting systems leveraging smart contracts are increasingly crucial. Blockchain, known for its transparency and security, faces challenges in the efficiency and security of smart contracts, necessitating innovative solutions. This research explores the potential of Natural Language Processing (NLP) technology such as BERT to enhance these aspects within smart contracts. Grounded in blockchain and smart contract theories, the study investigates BERT's use in detecting financial fraud and improving transaction security. The research methodology employs Bibliometric Analysis with a PICO approach to formulate research questions and gather relevant data. Bibliometric analysis provides deeper insights into current developments and challenges in this field. The analysis reveals trends in document production, author collaborations, and keywords related to blockchain and BERT. Co-occurrence networks and thematic maps highlight the significance and relevance of these terms in the context of blockchain and smart contracts. In conclusion, this study makes a significant contribution to understanding how NLP technology can be optimized to enhance the efficiency and security of smart contracts in blockchain-based accounting systems. Further research is essential to develop BERT optimization in this context.