Wazir Zada Khan, Ayesha Siddiqa, Faisal Alanazi, Muhammad Khurram Khan
The Metaverse creates a 3D virtual environment similar to the real world, enabling immersive interactions across diverse fields such as education, healthcare, and gaming. A critical aspect of these interactions is digital identity authentication, which ensures secure and trustworthy user experiences. This paper proposes a novel Non-Fungible Token (NFT) based digital identity authentication framework for the Metaverse, leveraging blockchain technology and Elliptic Curve Cryptography (ECC) to enhance security and user trust. The framework is tested using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, demonstrating its resilience against replay and man-in-the-middle (MITM) attacks. Our contributions include:(1)a secure NFT-based authentication mechanism,(2)a formal security analysis validating the framework’s robustness, and(3)a comprehensive discussion of practical implications. The proposed framework addresses key gaps in existing methods, offering a scalable and user-friendly solution for digital identity authentication in the Metaverse.
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.
Blockchain technology, known for its decentralized, tamper-proof, and secure transparency characteristics, has been widely applied in distributed ledger systems. The consensus mechanism, a core component of blockchain, has attracted significant research attention. Existing studies on improving the Delegated Proof of Stake (DPoS) consensus mechanism are primarily conducted in simulated environments, lacking practical implementation on production blockchain platforms. This paper applies an improved DPoS consensus mechanism based on the vague sets of the EOSIO blockchain platform. By optimizing the underlying code and smart contracts, the functionality of casting dissenting votes by voting nodes is introduced. The voting outcomes are expressed using vague sets, and a transformation model between vague sets and fuzzy sets is utilized to calculate the fuzzy membership degrees of candidate nodes. These degrees are then used to rank the candidates and select block producers, significantly enhancing the fairness and rationality of the voting process and enabling a more intelligent node selection process. In this study, 25 server nodes were deployed across five global cities on the Huawei Cloud platform to construct a fully functional EOSIO blockchain. Experimental results demonstrate that all nodes in the improved EOSIO system are capable of synchronously displaying block information and achieving consensus. The proposed enhanced EOSIO system exhibits the same efficient consensus performance and more flexible voting mechanism, offering a novel solution for the practical application of blockchain technology.
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.
Traditional Supervisory Control and Data Acquisition (SCADA) are prone to cyber attacks which makes it difficult in keeping data secure. As the traditional architecture relies on centralized storage, which makes them vulnerable to unauthorized access and manipulation. To tackle these security concerns, the study demostrates the integration of blockchain technology and Bulletproof, a zero-knowledge range proof technique into SCADA systems to securely store aggregated sensor value proof in the ledger. The study uses Hyperledger fabric, creating a transparent and tamper-proof record for audits and providing a more secure and trustworthy system. This approach reduces storage and computational overhead while ensuring that data remains private and within valid ranges without exposing sensitive details. By combining blockchain with zero-knowledge range proofs, the proposed solution enhances both security and efficiency in SCADA systems. It ensures that sensor data remains protected from tampering while keeping blockchain resources optimized. This makes industrial systems more secure, reliable, and ready for the future of automation.
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.
The Internet of Vehicles facilitates seamless Vehicle-to-Everything (V2X) communication, offering a myriad of services ranging from traffic management to data exchange and route scheduling. However, the existence of malicious Autonomous Vehicles (AVs) poses significant security and privacy threats to data communications and vehicle users, respectively. Therefore, it is crucial to verify the identity and preserve the privacy of AVs before offering V2X services within each vehicular broadcast domain. To address the aforementioned issues, a novel privacy-preserving lightweight Fast Reed-Solomon Interactive Oracle Proof of Proximity using polynomial commitment-based authentication protocol is presented. The AVs are initially registered with a trusted authority in this protocol. After that, they are authenticated by roadside units in their respective broadcast domains using a zero-knowledge proof-based challenge-response mechanism. As per the performance analysis, the proposed protocol surpasses state-of-the-art authentication protocols and achieves notable improvements of 19.43% in registration computation time, 50.96% in registration latency, 89.75% in authentication computation time, 14.97% in authentication latency, 97.42% in handover computation time, and 95.84% in handover latency, compared to other protocols. A qualitative security analysis is also carried out to prove that the proposed protocol provides anonymity, privacy, user verifiability, and untraceability features.
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.
H.C. Zhang, Shike Li, Shike Li, Hang Bao · 6 authors
The rapid development of blockchain technology has driven the widespread application of decentralized applications (DApps) across various fields. However, DApps cannot directly access external data and rely on oracles to interact with off-chain data. As a bridge between blockchain and external data sources, oracles pose potential risks of malicious behavior, which may inject incorrect or harmful data, leading to trust and security issues. Additionally, with the surge in data requests, the disparity in oracle trustworthiness and costs has increased, making the dynamic selection of the most suitable oracle for each request a critical challenge. To address these issues, this paper proposes a Trust-Aware and Cost-Optimized Blockchain Oracle Selection Model with Deep Reinforcement Learning (TCO-DRL). The model incorporates a comprehensive trust management mechanism to evaluate oracle reputation from multiple dimensions and employs an improved sliding time window to monitor reputation changes in real time, enhancing resistance to malicious attacks. Moreover, TCO-DRL uses deep reinforcement learning algorithms to dynamically adapt to fluctuations in oracle reputation, ensuring the selection of high-reputation oracles while optimizing node selection, thereby reducing costs without compromising data quality. We implemented and validated TCO- DRL on Ethereum. Experimental results show that, compared to existing methods, TCO-DRL reduces the allocation rate to malicious oracles by more than 39.10% and saves over 12.00% in costs. Furthermore, simulated experiments on various malicious attacks further validate the robustness and effectiveness of TCO-DRL
This article explores the transformative potential of blockchain technology in maintaining test data integrity across regulated industries, particularly in finance, healthcare, and pharmaceuticals. The article examines how blockchain's inherent characteristics address critical challenges in data security, compliance, and operational efficiency. Through analysis of implementation strategies, industry-specific applications, and emerging trends, the article demonstrates how blockchain technology revolutionizes test data management through immutable record-keeping, decentralized architecture, and smart contract automation. The article indicates significant improvements in data security, compliance management, and operational efficiency across all examined sectors, while highlighting the importance of structured implementation approaches and industry-specific considerations.
Dayong Pu, Tao Li, Zhaoqi Jin, Shihan Liu · 5 authors
With the development of the Internet of Things (IoT) and intelligent transportation systems, networked toll systems have become an essential part of modern traffic management. However, traditional identity authentication mechanisms rely on centralized trust institutions, which are prone to issues such as vulnerability to data storage attacks, identity information leakage, and poor interoperability between systems. These problems not only compromise the security and stability of toll systems but also risk user privacy and economic losses. To address these challenges, this paper proposes a distributed identity authentication mechanism based on blockchain technology. This mechanism leverages the decentralized, tamper-resistant, and smart contract features of blockchain technology to achieve an identity authentication process without the involvement of centralized institutions by storing user identity information in a distributed manner on the blockchain. The system utilizes distributed ledger technology to record identity authentication information across multiple nodes, thereby eliminating the risk of single-point failures and ensuring data consistency and integrity through consensus algorithms. Additionally, an automated identity authentication process based on smart contracts is designed to enable efficient identity verification between different networked toll subsystems, ensuring the security of the authentication process and consistency of user experience. The blockchain-based distributed identity authentication mechanism not only enhances the overall security level of networked toll systems but also provides a new solution for identity authentication in intelligent transportation systems.