Computer Standards & Interfaces最新文献

{"title":"Breaking barriers in healthcare: A secure identity framework for seamless access","authors":"Antonio López Martínez ,&nbsp;Montassar Naghmouchi ,&nbsp;Maryline Laurent ,&nbsp;Joaquín García Alfaro ,&nbsp;Manuel Gil Pérez ,&nbsp;Antonio Ruiz Martínez","doi":"10.1016/j.csi.2025.104020","DOIUrl":"10.1016/j.csi.2025.104020","url":null,"abstract":"<div><div>The digitization of healthcare data has heightened concerns about security, privacy, and interoperability. Traditional centralized systems are vulnerable to cyberattacks and data breaches, risking the exposure of sensitive patient information and decreasing trust in digital healthcare services. In addition, healthcare stakeholders use various standards and formats, creating challenges for data sharing and seamless communication. To address these points, this article identifies all the healthcare stakeholders and translates each useful element of a patient’s electronic health record (EHR) into Fast Healthcare Interoperability Resources (FHIR), to propose a complete role-based access control model that specifies which FHIR resources an actor is allowed to access. To validate this role model, three new use cases are defined, in which the various stakeholders interact and access the FHIR resources. Moreover, specific smart contracts are detailed to implement the role model in an automated way and provide a robust access control mechanism within healthcare organizations. The feasibility of the proposed access control mechanism is demonstrated through proof-of-concept and test performance measurements. Finally, the solution is validated as a realistic solution adapted to the scale of a country based on health statistics.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104020"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality assessment of GPT-3.5 and Gemini 1.0 Pro for SQL syntax","authors":"Cosmina-Mihaela Rosca ,&nbsp;Adrian Stancu","doi":"10.1016/j.csi.2025.104041","DOIUrl":"10.1016/j.csi.2025.104041","url":null,"abstract":"<div><div>Nowadays, GPT-3.5 and Gemini 1.0 Pro are employed for various tasks, both for personal and professional use, in multiple domains like education, economy, computer science, etc. Given the increase in users, knowing the quality level of these artificial intelligence (AI) tools is important. Thus, the paper presents a comparative analysis of syntax accuracy generated for SQL databases utilizing the services of GPT-3.5 and Gemini 1.0 Pro. Firstly, the algorithms for testing GPT-3.5 and Gemini 1.0 Pro were developed. Secondly, five types of tests, which implied 700 queries, were conducted by considering requirements with low and high degrees of difficulty. The tests focus on syntax-generated accuracy using an experimental (NorthWind) database, syntax-generated accuracy study using a user-made database, syntax correction accuracy, different responses to the same question on the same account, and different responses to the same question on other accounts. The accuracy obtained for all tests revealed that the GPT-3.5 service has a value of 87 % for SQL syntax generation or correction, whereas the Gemini 1.0 Pro service has an accuracy of 80 %. These results underscore the effectiveness of GPT-3.5 and Gemini 1.0 Pro in assisting with SQL syntax tasks, albeit with differing levels of precision. The findings highlight the significance of human supervision and validation in ensuring the correctness of AI-generated responses, particularly in database-related tasks. The results affect developers and database administrators when selecting appropriate tools for query requirements. For now, replacing programmers with GPT-3.5 and Gemini 1.0 Pro is impossible.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104041"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"USBIPS framework: Protecting hosts from malicious USB peripherals","authors":"Chun-Yi Wang ,&nbsp;Fu-Hau Hsu","doi":"10.1016/j.csi.2025.104040","DOIUrl":"10.1016/j.csi.2025.104040","url":null,"abstract":"<div><div>Universal Serial Bus (USB)-based attacks have increased in complexity in recent years. Modern attacks incorporate a wide range of attack vectors, from social engineering to signal injection. The security community is addressing these challenges using a growing set of fragmented defenses. Regardless of the vector of a USB-based attack, the most important risks concerning most people and enterprises are service crashes and data loss. The host OS manages USB peripherals, and malicious USB peripherals, such as those infected with BadUSB, can crash a service or steal data from the OS. Although USB firewalls have been proposed to thwart malicious USB peripherals, such as USBFilter and USBGuard, their effect is limited for preventing real-world intrusions. This paper focuses on building a security framework called USBIPS within Windows OSs to defend against malicious USB peripherals. This includes major efforts to explore the nature of malicious behavior and achieve persistent protection from USB-based intrusions. Herein, we first introduce an allowlisting-based method for USB access control. We then present a behavior-based detection mechanism focusing on attacks integrated into USB peripherals. Finally, we propose a novel approach that combines cross-layer methods to build the first generic security framework that thwarts USB-based intrusions. Within a centralized threat analysis framework, the approach provides persistent protection and may detect unknown malicious behavior. By addressing key security and performance challenges, these efforts help modern OSs against attacks from untrusted USB peripherals.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104040"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiuser data integrity auditing atop blockchain with secure user revocation for cognitive IoT networks","authors":"Min Xie ,&nbsp;Yong Yu ,&nbsp;Ruonan Chen ,&nbsp;Yanqi Zhao ,&nbsp;Jianting Ning ,&nbsp;Xiaoyi Yang ,&nbsp;Zoe L. Jiang","doi":"10.1016/j.csi.2025.104042","DOIUrl":"10.1016/j.csi.2025.104042","url":null,"abstract":"<div><div>Cognitive computing over big data has advanced the cognitive Internet of Things (IoT), enhancing adaptive decision-making by the analysis of shared data, while posing challenges in the storage and multi-user sharing of large-scale real-time data. Decentralized cloud storage is a promising solution to reduce latency and prevent single-point failures, but a key factor in preventing erroneous decisions lies in the integrity of shared data. However, integrity auditing in decentralized and shared storage typically involves linear overheads, with revoked users potentially colluding with cloud providers to evade audits. To address these issues, we propose a blockchain-based multiuser data integrity auditing protocol for cognitive IoT, supporting secure user revocation and batch auditing in decentralized storage. Our protocol classifies group users into different categories and manages group users efficiently and dynamically. Besides, files stored across various storage nodes can be audited in batches and effectively updated based on novel block tags. Smart contracts deployed on the blockchain ensure fairness among participants. We formally prove the security of the protocol in the random oracle model and the algebraic group model, in particular, against collusion attacks. Finally, we evaluate the cost separately for on-chain operations and off-chain operations to show its practicality.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104042"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyber hygiene of SMiShing: What they know and where they look","authors":"Morgan E. Edwards,&nbsp;Jeremiah D. Still","doi":"10.1016/j.csi.2025.104048","DOIUrl":"10.1016/j.csi.2025.104048","url":null,"abstract":"<div><div>Phishing attacks exploit psychological vulnerabilities to steal valuable information. While extensive research has improved defenses against email phishing, less is known about how non-technical users, particularly young adults, respond to SMiShing (SMS phishing) attacks. This research addresses this empirical gap by investigating user behavior and decision-making processes related to SMiShing among undergraduate students, a demographic identified as particularly susceptible to these attacks. Study 1 surveyed college students' SMiShing knowledge, experience, and hygiene practices, examining the influence of traditional phishing susceptibility factors. Contrary to expectations, these factors were not predictive of SMiShing behavior. Furthermore, the Cyber Hygiene Inventory (CHI) proved ineffective in predicting secure SMiShing practices. Study 2 combined eye-tracking and self-reported data to analyze how users evaluate text message legitimacy. While participants accurately reported using message content, they over-reported their reliance on sender phone numbers. These findings provide crucial insights into the SMiShing attack vector from the end-user perspective, specifically within a vulnerable demographic. This work highlights the need for human-centered security solutions tailored to the unique challenges of SMiShing, ultimately improving user resilience against these attacks.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104048"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a new standard for network access authentication: EAP-EDHOC","authors":"Francisco Lopez-Gomez ,&nbsp;Rafael Marin-Lopez ,&nbsp;Gabriel Lopez-Millan ,&nbsp;Dan Garcia-Carrillo ,&nbsp;John Preuß Mattsson ,&nbsp;Göran Selander","doi":"10.1016/j.csi.2025.104037","DOIUrl":"10.1016/j.csi.2025.104037","url":null,"abstract":"<div><div>The Extensible Authentication Protocol (EAP) has been a cornerstone of secure authentication in both wired and wireless networks, as well as enterprise systems, enabling integration with a wide range of authentication mechanisms. Recently, the IETF EAP Method Update (EMU) Working Group has adopted EAP-EDHOC, a method that combines EAP’s extensibility with the recent standard Ephemeral Diffie–Hellman Over COSE (EDHOC). EDHOC is a lightweight authentication and key exchange protocol designed to be supported in resource-constrained environments. This enhances EAP-EDHOC as a high-performance authentication method for EAP-based networks. This paper presents a comprehensive analysis of the standardization efforts surrounding EAP-EDHOC, including a first proof-of-concept implementation and performance evaluation conducted over Wi-Fi networks. Additionally, a new design that optimizes the existing protocol by reversing the roles of the communication parties is proposed. The original and optimized versions are evaluated and compared with each other, as well as with EAP-TLS 1.3 and EAP-PSK. The results demonstrate that EAP-EDHOC achieves more efficient authentication than EAP-TLS 1.3 in terms of execution time, number of messages, and data transmitted. Meanwhile, EAP-PSK, which is based on symmetric cryptography, serves as a performance baseline.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104037"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel clustering approach for recommendation systems using adaptive fuzzy clustering with Jensen–Shannon divergence","authors":"Gökhan Kayhan,&nbsp;Naciye Aydin,&nbsp;Sercan Demirci","doi":"10.1016/j.csi.2025.104035","DOIUrl":"10.1016/j.csi.2025.104035","url":null,"abstract":"<div><div>In today’s world, where users are surrounded by a multitude of products, recommender systems are employed to assist users in finding products of interest. Clustering methods are frequently utilized in recommender systems to suggest relevant products. Fuzzy clustering techniques, one of the most commonly used clustering methods, determine the degree of relevance of each product to a cluster through the membership matrix it generates. However, determining the number of clusters in these methods poses a challenge. This study proposes an Adaptive Fuzzy C-Means Jensen Shannon (AFCM-JS) algorithm, a fuzzy and interest-based clustering method that estimates the number of clusters. The proposed AFCM-JS algorithm is implemented on an artificial dataset consisting of 6 clusters and 1000 elements. The results of the study are compared with Fuzzy C-Means (FCM), Probabilistic C-Means (PCM), and Probabilistic Fuzzy C-Means (PFCM) methods, which are fuzzy-based clustering algorithms, and the interest-based method JS. To evaluate the comparison results, 7 different cluster validity indices and an accuracy metric are employed. AFCM-JS method consistently and accurately predicted the number of clusters when tested with different maximum cluster numbers. When the clustering ability of the method is tested with cluster validity indices and the accuracy metric, AFCM-JS is found to be successful. The performance of the AFCM-JS method is tested on a dataset created for a movie recommendation system with the aim of recommending movies to users. For this purpose, movie data is weighted with a Dirichlet function for action, adventure, comedy, drama, and horror genres, creating a dataset that includes the characteristics of these 5 movie genres. The AFCM-JS method is compared with 3 different fuzzy clustering methods using 7 different cluster validity indices with this created movie dataset. Additionally, the AFCM-JS algorithm is compared with the other 3 fuzzy clustering methods based on the accuracy metric. As a result of this comparison, the AFCM-JS method achieves the highest performance among the methods with 81.9366%. Furthermore, when the performance of the proposed method is compared in terms of cluster validity indices, the AFCM-JS method successfully predicts the appropriate number of clusters and effectively groups similar movies according to their genres, accomplishing the purpose.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104035"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-aided secure and fair multi-view data outsourcing computation scheme","authors":"Xinrong Sun ,&nbsp;Fanyu Kong ,&nbsp;Yunting Tao ,&nbsp;Pengyu Cui ,&nbsp;Guoyan Zhang ,&nbsp;Chunpeng Ge ,&nbsp;Baodong Qin","doi":"10.1016/j.csi.2025.104029","DOIUrl":"10.1016/j.csi.2025.104029","url":null,"abstract":"<div><div>With the widespread deployment of smart sensors, multi-view data has been widely used. Accordingly, multi-view processing algorithms are increasingly researched, among which the cluster-weighted kernel k-means method is an effective approach to dig up information of different views. However, large-scale multi-view data make it difficult to conduct processing algorithms. Therefore, outsourcing complex computations to servers based on privacy-preserving techniques is an effective solution that enables efficient multi-view data analysis. In previous secure outsourcing schemes, the efficiency of the outsourcing process and the fairness of outsourcing transactions are still challenging issues that have not been addressed. In this paper, we propose a blockchain-aided secure and fair multi-view data outsourcing computation scheme. We present an efficient matrix encryption method utilizing a novel secret key matrix to complete cluster-weighted kernel k-means algorithm securely. Different from previous works, we first apply the sparse symmetric orthogonal matrix to encrypt and decrypt sensitive data matrices, which avoids inverse or transposed secret key matrix computation and enhances the efficiency of the outsourcing process. Additionally, we introduce smart contracts to achieve fair outsourcing transactions aided by blockchain. We verify the returned result with the assistance of verifiers based on encrypted data, which improves the efficiency and security of outsourcing transactions. The experimental results indicate that our scheme is 4.72% to 8.52% superior to the state-of-the-art matrix outsourcing computation schemes and achieves 55.79% to 91.95% efficiency improvement compared to the original multi-view data processing method.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104029"},"PeriodicalIF":4.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new multi-image encryption scheme for Smart Home IoT integrating hyperchaos and compressive sensing","authors":"Yuanmao Zhong,&nbsp;Qiang Lai,&nbsp;Chongkun Zhu,&nbsp;Minghong Qin","doi":"10.1016/j.csi.2025.104051","DOIUrl":"10.1016/j.csi.2025.104051","url":null,"abstract":"<div><div>The rapid development of Smart Home IoT (SH-IoT) technologies presents considerable challenges in information security and privacy protection, including recurrent data breaches and privacy violations. Addressing these issues, this study introduces a multi-image encryption algorithm utilizing a novel 3D discrete hyperchaotic map to strengthen SH-IoT security. The solution simultaneously encrypts multiple images by integrating compressive sensing, while novel encryption units disrupt pixel correlations through cross-plane permutation and ring chain diffusion. Demonstrating remarkable adaptability, the algorithm dynamically adjusts compression ratios according to device capabilities and application demands, optimizing the security-efficiency-quality balance. Experimental validation confirms exceptional performance: achieving 99.6095% NPCR and 33.4597% UACI, along with a 2481 kb/s encryption speed at 0.5 compression ratio—substantially outperforming non-compressed scenarios.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104051"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMART-DEFENSE: 3D hexagonal federated network for systematic attack detection in blockchain-integrated healthcare environment","authors":"Gobalakrishnan N ,&nbsp;Muthu Pandeeswari R","doi":"10.1016/j.csi.2025.104053","DOIUrl":"10.1016/j.csi.2025.104053","url":null,"abstract":"<div><div>Ensuring the security of healthcare systems is crucial for protecting sensitive patient data from breaches and unauthorized access, thereby preserving confidentiality and maintaining trust in medical institutions. Robust security measures are also vital in preventing medical identity theft, which can result in fraudulent treatments and financial losses for patients. However, existing security frameworks often suffer from critical shortcomings, including weak encryption methods, inadequate access controls, and ineffective incident response mechanisms, leaving systems vulnerable to cyber threats. Additionally, many traditional security solutions lack adaptability to emerging technologies such as telemedicine and the Internet of Things (IoT), which introduce new attack vectors if not properly managed. To address these challenges, we introduce <strong>SMART-DEFENSE</strong> (Secure Medical Architecture for Resilient Trust and Dynamic Encryption in Federated Edge Networks with Security Enhancements), a novel defense model specifically designed for smart healthcare environments. The proposed framework consists of key components, including medical IoT devices, a Detective Gateway (DG), Edge Servers with Honeypot (HES), a Blockchain-assisted Cloud Server (BCS), and a Trust Integrity Authority (TIA). To enhance scalability, a 3D hexagonal-based network architecture (3D-Hexa) is implemented as the foundation of the system. Security is reinforced through Decentralized Identity Management and Dynamic Trust Scoring, incorporating Shuffled Pass Points (SPP) for authentication and Optimizer-based Trust Evaluation (OTE) via the DG. To improve threat detection accuracy<strong>,</strong> Stratified Federated Learning (SFL) is employed within the HES, enabling a collaborative defense mechanism against cyberattacks. Finally, Chaotic Encryption Blockchain (CEB) ensures secure data management by strengthening both privacy and integrity. By integrating these cutting-edge technologies, SMART-DEFENSE provides a comprehensive, resilient, and scalable security framework for smart healthcare achieving a 28.5 % reduction in False Acceptance Rate, a 16.9 % increase in authentication success rate, and a 4.15 % improvement in trust evaluation accuracy over existing models effectively mitigating cyber threats while ensuring the privacy and reliability of healthcare data.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"95 ","pages":"Article 104053"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}