Web3 technologies have experienced unprecedented growth in the last decade, achieving widespread adoption. As various blockchain networks continue to evolve, we are on the cusp of a paradigm shift in which they could provide services traditionally offered by the Internet, but in a decentralized manner, marking the emergence of the Internet of Blockchains. While significant progress has been achieved in enabling interoperability between blockchain networks, existing solutions often assume that networks are already mutually aware. This reveals a critical gap: the initial discovery of blockchain networks remains largely unaddressed. This paper proposes a decentralized architecture for blockchain network discovery that operates independently of any centralized authority. We also introduce a mechanism for discovering assets and services within a blockchain from external networks. Given the decentralized nature of the proposed discovery architecture, we design an incentive mechanism to encourage nodes to actively participate in maintaining the discovery network. The proposed architecture implemented and evaluated, using the Substrate framework, demonstrates its resilience and scalability, effectively handling up to 130,000 concurrent requests under the tested network configurations, with a median response time of 5.5 milliseconds, demonstrating the ability to scale its processing capacity further by increasing its network size.
The decentralized finance (DeFi) community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of the technology makes the task of catching and prosecuting offenders particularly challenging. Thus, it is necessary to implement automated detection tools related to policies to address the growing criminality in the cryptocurrency realm.
Bitcoin mining is highly energy-intensive, and improving its efficiency is critical for both economic and environmental sustainability. This project presents a web-based simulation tool that models key aspects of Bitcoin mining, including the SHA-256 hashing algorithm, nonce iteration, and target difficulty checks. The computational backend is integrated with real-time power and thermal models, enabling the simulator to reflect how hash rate influences energy consumption and temperature. Interactive controls for frequency, voltage, and resistance, along with graphical visualizations of power usage over time, allow users to explore trade-offs between energy efficiency and mining performance. The simulation also includes a financial trade-off analysis feature and supports extended runtime testing to evaluate long-term behavior under varying operational conditions.
Ensuring software quality in the Web3 ecosystem presents unique challenges due to its decentralized architecture and evolving technical landscape. While international standards such as the SQuaRE (Systems and software Quality Requirements and Evaluation) framework offer structured approaches for quality assurance, they are often perceived as overly theoretical and not directly applicable to blockchain-based applications. This study aims to translate these standards into actionable practices suitable for Web3 environments, thereby supporting compliance and fostering stakeholder trust. Using the Design Science Research methodology, complemented by Lean Startup principles, a practical quality assurance guide was co-developed through collaboration between VOH.CoLAB researchers and the Exeedme project team and inspired by the practical experience in gaming and digital assets trading blockchain-based platforms. The resulting guide includes a structured framework comprising eight testing domains, 16 sub-domains and 108 targeted tests, with the domains addressing critical features of blockchain software, including, functional suitability, integration, security, performance, usability, portability, recoverability and resilience. This work contributes to the operationalization of international quality standards in decentralized technology, promoting more resilient and trustworthy blockchain applications.
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae‐Min Lee
Increased automation, connectivity, and data sharing enabled by 6G technology have heightened the vulnerability of Internet of Vehicles (IoV) networks. Addressing this challenge requires an intrusion detection system (IDS) capable of accurately identifying data falsification within IoV while adhering to real-time constraints. This paper presents a Blockchain-enhanced feature-engineered IDS to ensure precise attack detection and classification with minimal computational overhead in in-vehicle networks (IVNs). The proposed lightweight IDS utilizes a hybrid Pearson’s Correlation Coefficient (PCC) feature selection technique designed for deployment on the Telematics Control Unit (TCU). Furthermore, we propose a custom private blockchain network utilizing the proof of authority and association (PoA) consensus mechanism, deployable on the Roadside Unit (RSU), for the secure logging of vehicle Electronic Control Unit (ECU) information, detection results, and the automatic isolation of malicious ECUs via smart contracts. Experimentation analysis demonstrates that the proposed approach achieves notable performance, with a 99.9% detection accuracy and minimal computation times of 0.24s and 1.32s on the CICIoV2024 and CAN-Intrusion datasets. Furthermore, the system achieves high blockchain scalability, maintaining stable throughput of 16 tx/s and low transaction latency of 0.062s under increasing ECU density and RSU coverage.
Mutiullah Shaikh, Shafique Memon, Ali Ebrahimi, Uffe Kock Wiil
BACKGROUND: Healthcare information systems are hindered by delayed data sharing, privacy breaches, and lack of patient control over data. The growing need for secure, privacy-preserved access control interoperable in health informatics technology (HIT) systems appeals to solutions such as Blockchain (BC), which offers a decentralized, transparent, and immutable ledger architecture. However, its current adoption remains limited to conceptual or proofs-of-concept (PoCs), often relying on simulated datasets rather than validated real-world data or scenarios, necessitating further research into its pragmatic applications and their benchmarking. OBJECTIVE: This systematic literature review (SLR) aims to analyze BC-based healthcare implementations by benchmarking peer-reviewed studies and turning PoCs or production insights into real-world applications and their evaluation metrics. Unlike prior SLRs focusing on proposed or conceptual models, simulations, or limited-scale deployments, this review focuses on validating practical BC real-world applications in healthcare settings beyond conceptual studies and PoCs. METHODS: Adhering to PRISMA-2020 guidelines, we systematically searched five major databases (Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect) for high-precision relevant studies using MeSH terms related to BC in healthcare. The designed review protocol was registered with OSF, ensuring transparency in the review process, including study screening by independent reviewers, eligibility, quality assessment, and data extraction and synthesis. RESULTS: In total, 82 original studies fully met the eligibility criteria and narratively reported BC-based healthcare implementations with validated evaluation outcomes. These studies highlight the current challenges addressed by BC in healthcare settings, providing both qualitative and quantitative data synthesis on its effectiveness. CONCLUSIONS: BC-based healthcare implementations show both qualitative and quantitative effectiveness, with advancements in areas such as drug traceability (up to 100%) and fraud prevention (95% reduction). We also discussed the recent challenges of focusing more attention in this area, along with a discussion on the mythological consideration of our own work. Our future research should focus on addressing scalability, privacy-preservation, security, integration, and ethical frameworks for widespread BC adoption for data-driven healthcare.
The rapid growth of intelligent systems has raised significant concerns regarding data privacy and security. Traditional centralized machine learning approaches require data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training while keeping data decentralized. This paper presents a comprehensive study of federated learning for privacy-preserving intelligent systems, highlighting its architecture, methodologies, applications, and challenges. The study also proposes an adaptive federated framework integrating secure aggregation and differential privacy. The findings demonstrate that federated learning significantly enhances privacy while maintaining model performance, making it suitable for healthcare, finance, and IoT applications.
This work focuses on multi-dimensional approach to incorporate the Support Vector Machine (SVM) models with Blockchain to secure distributed ledger against APTs.The classifying and high pattern recognition ability of SVM makes the proposed framework easily capture and neutralize malicious activities in the blockchain networks in realtime.The distribution of the blockchain technology and use of machine learning for predictive modeling guarantees a hard-coded countermeasure against new forms of cyber threats.As such, this work is centered on how these technologies can be integrated in harmony: attempting to enhance the accuracy of threat identification without compromising the functionality of the blockchain.This implementation shows the possibility of achieving strong, secure and scalable applications in different applications domains, and so make a way forward for upcoming decentralized cybersecurity solutions.
The rapid expansion of digital platforms has fundamentally reshaped the way organizations exchange information, manage transactions, and coordinate distributed operations. However, the increasing dependence on centralized digital infrastructures has also raised concerns regarding data integrity, system transparency, and institutional trust. Traditional software architectures often rely on centralized authorities to validate transactions, manage data ownership, and enforce operational rules. While these systems have enabled large-scale digital services, they also introduce risks related to single points of failure, data manipulation, and institutional dependency. Blockchain technology has emerged as a transformative approach for addressing these challenges by enabling distributed trust infrastructures that operate without centralized intermediaries. By combining cryptographic security, distributed consensus mechanisms, and immutable ledgers, blockchain platforms provide a foundation for building software systems in which trust is established through verifiable computation rather than institutional authority. This capability has attracted significant interest from both academic researchers and industry practitioners seeking to design secure digital infrastructures for financial services, supply chains, identity management, and data governance. Despite the conceptual appeal of blockchain technology, integrating distributed ledger infrastructures into modern software platforms presents substantial engineering challenges. Enterprise software systems must operate at high levels of scalability, maintain strong security guarantees, and support integration with existing digital infrastructures. Designing blockchain-integrated service platforms therefore requires a careful balance between decentralized trust mechanisms and practical software engineering constraints. This paper examines the architectural foundations required for engineering blockchain-integrated service platforms capable of supporting secure distributed trust systems. The study analyzes how blockchain technologies can be integrated into modern software architectures, explores the role of smart contracts as programmable trust mechanisms, and investigates the security implications of distributed ledger infrastructures. Particular attention is given to architectural design patterns that enable blockchain systems to operate alongside conventional cloud-based service architectures. The paper further discusses scalability challenges associated with blockchain networks and explores strategies for integrating distributed ledger technologies with enterprise software platforms. Through a comprehensive architectural analysis, this research proposes design principles for building secure, scalable, and resilient blockchain-enabled service platforms. By examining the intersection of distributed systems engineering, cryptographic security, and software architecture design, this study contributes to a deeper understanding of how blockchain technologies can support the development of trustworthy digital infrastructures for next-generation software systems.
Blockchain technology has set off a wave of decentralization in the world since its birth. The trust system constructed by blockchain technology based on cryptography algorithm and computing power provides a practical and powerful solution to solve the trust problem in human society. In order to make more convenient use of the characteristics of blockchain and build applications on it, smart contracts appear. By defining some trigger automatic execution contracts, the application space of blockchain is expanded and the foundation for the rapid development of blockchain is laid. This is blockchain 2.0. However, the programmability of smart contracts also introduces vulnerabilities. In order to cope with the insufficient security guarantee of high-value application networks running on blockchain 2.0 and smart contracts, this article will be represented by Ethereum to introduce the technical details of understanding blockchain 2.0 and the operation principle of contract virtual machines, and explain how cryptocurrencies based on blockchain 2.0 are constructed and operated. The common security problems and solutions are also discussed. Based on relevant research and on-chain practice, this paper provides a complete and comprehensive perspective to understanding cryptocurrency technology based on blockchain 2.0 and provides a reference for building more secure cryptocurrency contracts.
With the booming development of blockchain technology, smart contracts have been widely used in finance, supply chain, Internet of things and other fields in recent years. However, the security problems of smart contracts become increasingly prominent. Security events caused by smart contracts occur frequently, and the existence of malicious codes may lead to the loss of user assets and system crash. In this paper, a simple study is carried out on malicious code detection of intelligent contracts based on machine learning. The main research work and achievements are as follows: Feature extraction and vectorization of smart contract are the first step to detect malicious code of smart contract by using machine learning method, and feature processing has an important impact on detection results. In this paper, an opcode vectorization method based on smart contract text is adopted. Based on considering the structural characteristics of contract opcodes, the opcodes are classified and simplified. Then, N-Gram (N=2) algorithm and TF-IDF algorithm are used to convert the simplified opcodes into vectors, and then put into the machine learning model for training. In contrast, N-Gram algorithm and TF-IDF algorithm are directly used to quantify opcodes and put into the machine learning model training. Judging which feature extraction method is better according to the training results. Finally, the classifier chain is applied to the intelligent contract malicious code detection.
Hang Yuan, Lei Yu, Zhirong Huang, Jingyuan Zhang · 10 authors
Smart contract vulnerabilities pose significant security risks to blockchain systems, potentially leading to severe financial losses. Existing methods face several limitations: (1) Program analysis-based approaches rely on predefined patterns, lacking flexibility for new vulnerability types; (2) Deep learning-based methods lack explanations; (3) Large language model-based approaches suffer from high false positives. We propose MOS, a smart contract vulnerability detection framework based on mixture-of-experts tuning (MOE-Tuning) of large language models. First, we conduct continual pre-training on a large-scale smart contract dataset to provide domain-enhanced initialization. Second, we construct a high-quality MOE-Tuning dataset through a multi-stage pipeline combining LLM generation and expert verification for reliable explanations. Third, we design a vulnerability-aware routing mechanism that activates the most relevant expert networks by analyzing code features and their matching degree with experts. Finally, we extend the feed-forward layers into multiple parallel expert networks, each specializing in specific vulnerability patterns. We employ a dual-objective loss function: one for optimizing detection and explanation performance, and another for ensuring reasonable distribution of vulnerability types to experts through entropy calculation. Experiments show that MOS significantly outperforms existing methods with average improvements of 6.32% in F1 score and 4.80% in accuracy. The vulnerability explanations achieve positive ratings (scores of 3-4 on a 4-point scale) of 82.96%, 85.21% and 94.58% for correctness, completeness, and conciseness through human and LLM evaluation.
Decentralised applications (dApps) that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be tracked and analysed, which can reveal user-behaviour information. A user behaviour analysis pipeline was proposed to present how this type of information can be extracted and analysed to identify separate behavioural clusters that can describe how users behave in the game. The pipeline starts with the collection of transaction data, involving smart contracts, that is collected from a blockchain-based game called Planet IX. Both the raw transaction information and the transaction events are considered in the data collection. From this data, separate game actions can be formed and those are leveraged to present how and when the users conducted their in-game activities in the form of user flows. An extended version of these user flows also presents how the Non-Fungible Tokens (NFTs) are being leveraged in the user actions. The latter is given as input for a Graph Neural Network (GNN) model to provide graph embeddings for these flows which then can be leveraged by clustering algorithms to cluster user behaviours into separate behavioural clusters. We benchmark and compare well-known clustering algorithms as a part of the proposed method. The user behaviour clusters were analysed and visualised in a graph format. It was found that behavioural information can be extracted regarding the users that belong to these clusters. Such information can be exploited by malicious users to their advantage. To demonstrate this, a privacy threat model was also presented based on the results that correspond to multiple potentially affected areas.
Peter Ince, Jiangshan Yu, Joseph K. Liu, Xiaoning Du
Recent years have seen an explosion of activity in Generative AI, specifically Large Language Models (LLMs), revolutionising applications across various fields. Smart contract vulnerability detection is no exception; as smart contracts exist on public chains and can have billions of dollars transacted daily, continuous improvement in vulnerability detection is crucial. This has led to many researchers investigating the usage of generative large language models (LLMs) to aid in detecting vulnerabilities in smart contracts. This paper presents a systematic review of the current LLM-based smart contract vulnerability detection tools, comparing them against traditional static and dynamic analysis tools Slither and Mythril. Our analysis highlights key areas where each performs better and shows that while these tools show promise, the LLM-based tools available for testing are not ready to replace more traditional tools. We conclude with recommendations on how LLMs are best used in the vulnerability detection process and offer insights for improving on the state-of-the-art via hybrid approaches and targeted pre-training of much smaller models.
Cryptocurrencies have transformed financial markets with their innovative blockchain technology and volatile price movements, presenting both challenges and opportunities for predictive analytics. Ethereum, being one of the leading cryptocurrencies, has experienced significant market fluctuations, making its price prediction an attractive yet complex problem. This paper presents a comprehensive study on the effectiveness of Large Language Models (LLMs) in predicting Ethereum prices for short-term and few-shot forecasting scenarios. The main challenge in training models for time series analysis is the lack of data. We address this by leveraging a novel approach that adapts existing pre-trained LLMs on natural language or images from billions of tokens to the unique characteristics of Ethereum price time series data. Through thorough experimentation and comparison with traditional and contemporary models, our results demonstrate that selectively freezing certain layers of pre-trained LLMs achieves state-of-the-art performance in this domain. This approach consistently surpasses benchmarks across multiple metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), demonstrating its effectiveness and robustness. Our research not only contributes to the existing body of knowledge on LLMs but also provides practical insights in the cryptocurrency prediction domain. The adaptability of pre-trained LLMs to handle the nature of Ethereum prices suggests a promising direction for future research, potentially including the integration of sentiment analysis to further refine forecasting accuracy.
Medical big data holds significant value in promoting precision medicine, disease prediction, and public health management. However, issues such as sensitivity, decentralization, and privacy security limit its in-depth application. This study proposes a collaborative computing framework based on blockchain and Apache Spark, aiming to address the challenges of privacy protection, cross-institutional sharing, and efficient analysis of medical data. By designing an access control mechanism based on smart contracts and an anonymization scheme utilizing zero-knowledge proofs, combined with Spark's distributed memory computing advantages, a secure and trustworthy platform for medical data analysis is constructed. Experiments demonstrate that this framework improves data processing efficiency by 3.5 times compared to the traditional Hadoop architecture on the MIMIC-III dataset, while also meeting HIPAA privacy standards. This study provides theoretical support and practical pathways for the application of "blockchain + big data" technology in the medical field.
Cryptocurrency is a novel exploration of a form of currency that proposes a decentralized electronic payment scheme based on blockchain technology and cryptographic theory. While cryptocurrency has the security characteristics of being distributed and tamper-proof, increasing market demand has led to a rise in malicious transactions and attacks, thereby exposing cryptocurrency to vulnerabilities, privacy issues, and security threats. Particularly concerning are the emerging types of attacks and threats, which have made securing cryptocurrency increasingly urgent. Therefore, this paper classifies existing cryptocurrency security threats and attacks into five fundamental categories based on the blockchain infrastructure and analyzes in detail the vulnerability principles exploited by each type of threat and attack. Additionally, the paper examines the attackers' logic and methods and successfully reproduces the vulnerabilities. Furthermore, the author summarizes the existing detection and defense solutions and evaluates them, all of which provide important references for ensuring the security of cryptocurrency. Finally, the paper discusses the future development trends of cryptocurrency, as well as the public challenges it may face.
Mrs. V. Deepapriya, C. Sathana, J. Rishwana Begam, V Rohini · 6 authors
Ensuring robust image security in cloud environments is a critical challenge due to risks such as unauthorized access, data tampering, and privacy breaches. This study introduces a Blockchain-based Secure Image Encryption (BC-SIE) method using Chebyshev Polynomial Fostered Hierarchical Auto-Associative Polynomial Convolutional Neural Network (CPHAPCNN) to enhance security, integrity, and high-fidelity image reconstruction. During encryption, the input image is divided into two unpredictable cryptographic shares, represented by black dot patterns, rendering them meaningless individually and preventing unauthorized access. These shares are then secured on a blockchain using an optimized BLAKE2b hashing algorithm, providing efficient and collision-resistant storage. Furthermore, the Chebyshev polynomial-based encryption strengthens security by introducing pixel scrambling, which makes the method resistant to cryptographic attacks. For decryption, the shares are recombined to reconstruct the image, but this introduces noise, impacting image quality. To mitigate this, a Hierarchical Auto-Associative Polynomial Convolutional Neural Network (HAPCNN) is utilized to reduce noise and preserve image details, ensuring near-lossless recovery. The performance of the BC-SIE-CPHAPCNN framework is evaluated using various metrics, including processing time, correlation coefficient, entropy, peak signal-to-noise ratio (PSNR: 28.44 dB), and mean square error (MSE). The results demonstrate superior encryption security and image reconstruction accuracy, with an updated computed SSIM accuracy of 91.75%. Additionally, the Delegated Proof of Stake (DT-DPoS) blockchain consensus mechanism enhances both security and scalability. Experimental evaluations confirm that this approach outperforms existing methods, making it ideal for cloud storage, medical imaging, and secure surveillance systems.
Blockchain technology is rapidly evolving, with scalability remaining one of its most significant challenges. While various solutions have been proposed and continue to be developed, it is essential to consider the blockchain trilemma -- balancing scalability, security, and decentralization -- when designing new approaches. One promising solution is the zero-knowledge proof (ZKP)-based rollup, implemented on top of Ethereum. However, the performance of these systems is often limited by the efficiency of the ZKP mechanism. This paper explores the performance of ZKP-based rollups, focusing on a solution built using the Hardhat Ethereum development environment. Through detailed analysis, the paper identifies and examines key bottlenecks within the ZKP system, providing insight into potential areas for optimization to enhance scalability and overall system performance.
Basem Mohamed Elomda, Taher Abouzaid Abdelaty Abdelbary, Hesham Hassan, Kamal S. Hamza · 5 authors
The Multi-Layer Blockchain Security Model (MLBSM) proposed in 2024 was designed to safeguard Internet of Things (IoT) networks, as well as similar network architectures, against transaction privacy leakage in public blockchain systems. MLBSM also addresses critical issues like latency, ensuring faster transaction speeds through clustering and parallel processing. This paper presents a new extension to the Multi-Layer Blockchain Security Model (MLBSM). The proposed model is called the Enhanced Multi-Layer Blockchain Security Model (EMLBSM). The proposed EMLBSM will solve latency issues by compressing and reducing the layers of the MLBSM through merging layer2 and layer3 in the MLBSM. This paper describes the required enhanced solution for latency and scalability problems that were found in the MLBSM.
The integration of Zero-Trust Architecture (ZTA) and Blockchain-based Security Models in IoT-driven industrial power electronics systems has emerged as a transformative approach to mitigating cyber threats and ensuring robust access control. Traditional security mechanisms, which rely on perimeter-based defenses, are increasingly ineffective against advanced persistent threats (APTs), insider attacks, and lateral movement techniques within industrial IoT (IIoT) environments. Zero-Trust security enforces continuous verification, least-privilege access, and micro-segmentation, ensuring that no device or user was inherently trusted. Implementing ZTA in resource-constrained IoT ecosystems presents significant challenges related to computational overhead, authentication latency, and secure data transmission. To address these limitations, blockchain technology enhances decentralized identity management, immutable access logs, and tamper-resistant security frameworks, fortifying Zero-Trust-based access control. Privacy-preserving cryptographic techniques, including zero-knowledge proofs (ZKPs) and homomorphic encryption, safeguard sensitive industrial data while maintaining compliance with evolving regulatory frameworks. AI-driven anomaly detection models reinforce continuous authentication and behavior-based threat monitoring, enabling proactive defense mechanisms against zero-day exploits and sophisticated cyber intrusions. This chapter presents a comprehensive analysis of Zero-Trust implementation models for IIoT systems, highlighting the role of secure communication protocols, distributed ledger-based identity verification, and adaptive security automation. The integration of blockchain-enabled access control and AI-powered real-time security analytics ensures a resilient security posture for industrial power electronics networks, mitigating risks associated with unauthorized access, data breaches, and operational disruptions. The proposed framework enhances scalability, privacy, and computational efficiency, paving the way for next-generation cybersecure industrial ecosystems.
The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data. Traditional methods, where everything was stored in one place, had big problems with keeping information private, handling large amounts of data, and avoiding unfair advantages. Machine Learning has become a powerful tool that uses Artificial Intelligence (AI) to overcome these challenges. We started by learning the basics of Machine Learning, including the different types like supervised, unsupervised, and reinforcement learning. We also explored the important steps involved, such as preparing the data, choosing the right model, training it, and then checking its performance. Next, we examined some key challenges in Machine Learning, such as models learning too much from specific examples (overfitting), not learning enough (underfitting), and reflecting biases in the data used. Moving beyond centralized systems, we looked at decentralized Machine Learning and its benefits, like keeping data private, getting answers faster, and using a wider variety of data sources. We then focused on a specific type called federated learning, where models are trained without directly sharing sensitive information. Real-world examples from healthcare and finance were used to show how collaborative Machine Learning can solve important problems while still protecting information security. Finally, we discussed challenges like communication efficiency, dealing with different types of data, and security. We also explored using a Zero Trust framework, which provides an extra layer of protection for collaborative Machine Learning systems. This approach is paving the way for a bright future for this groundbreaking technology.
In this paper, the third part of a comprehensive study on blockchain's role in intellectual property management, we interviewed 27 experts to explore blockchain's impact on managing the intellectual property life cycle that is how copyrights, trademarks, trade secrets, and patents are created, protected, managed, enforced, and monetized. Semi-structured interviews provided insights into the benefits and limitations of blockchain in managing intellectual property. Attributes were clustered using a meta-matrix validated by expert feedback. Participants shared views on barriers to blockchain adoption in intellectual property and predicted its evolution over the next 5–10 years. Our findings highlight advantages such as proof of authenticity and ownership, smart contracts, tokenization (including non-fungible tokens or NFTs), and legal protection. However, challenges like scalability, interoperability, lack of a blockchain-intellectual property ecosystem, and limited use cases must be addressed to foster adoption. We summarized implications and recommendations for future research.
The rapid growth of the blockchain ecosystem and the increasing value locked in smart contracts necessitate robust security measures. While languages like Solidity and Move aim to improve smart contract security, vulnerabilities persist. This paper presents Smartify, a novel multi-agent framework leveraging Large Language Models (LLMs) to automatically detect and repair vulnerabilities in Solidity and Move smart contracts. Unlike traditional methods that rely solely on vast pretraining datasets, Smartify employs a team of specialized agents working on different specially fine-tuned LLMs to analyze code based on the underlying programming concepts and language-specific security principles. We evaluated Smartify on a dataset for Solidity and a curated dataset for Move, demonstrating its effectiveness in fixing a wide range of vulnerabilities. Our experimental results show that Smartify (Gemma2+Codegemma) achieves state-of-the-art performance, surpassing existing LLMs and even enhancing the capabilities of general-purpose models, such as Llama 3.1. Notably, Smartify can incorporate language-specific knowledge, such as the nuances of Move, without requiring massive language-specific pretraining datasets. This work offers a detailed analysis of the performance of various LLMs on smart contract repair, highlighting the strengths of our multi-agent approach and providing a blueprint for developing more secure and reliable decentralized applications in the growing blockchain landscape. We also provide a detailed description to extend the proposed technology to other similar use cases.