Journal of Web Engineering最新文献

{"title":"A Web Engineering-Based Robust Watermark Restoration and Recognition Method for Protecting Online Video Content","authors":"Jieun Lee;Byeongchan Park;Uijin Jang;Yongtae Shin","doi":"10.13052/jwe1540-9589.2441","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2441","url":null,"abstract":"With the rapid expansion of over-the-top (OTT) services and web-based video streaming platforms, copyright protection has become a critical concern. Unauthorized redistribution and modification of digital content via composite transformations and distortions threaten content security. While watermarking and digital rights management (DRM) offer protection, existing methods often fail under real-world web-based attack scenarios. In this paper, we present a web engineering-based robust watermark restoration and recognition method to enhance the security of online video content. Our approach employs AKAZE feature detection to extract robust feature points, while a discrete wavelet transform (DWT) is used for subband decomposition, embedding the watermark in the lowest-energy subband near the detected feature points. To ensure resilience against distortions common in web environments, we evaluate our method under four types of noise (Gaussian, salt-and-pepper, uniform, and Poisson) and four rotation angles (0°, 90°, 180°, and 270°). AKAZE-based feature matching compensates for rotation distortions, while noise removal is handled using Gaussian, Median, or BM3D filtering. Performance evaluation using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation (NC), and bit error rate (BER) confirms the effectiveness of our method. Results show that BM3D filtering achieves the highest average NC (0.8996) and the lowest BER (0.1137), demonstrating strong robustness against composite transformation attacks. This study contributes to web-based video security by integrating feature-based watermarking techniques with web engineering principles, ensuring effective protection for modern web applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"473-498"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112761","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ISSF: An Intelligent Security Service Framework for Cloud-Native Operations","authors":"Yikuan Yan;Keman Huang;Michael Siegel","doi":"10.13052/jwe1540-9589.2447","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2447","url":null,"abstract":"The growing system complexity of microservice architectures and the bilateral enhancement of artificial intelligence (AI) for both attackers and defenders present increasing security challenges for cloud-native operations. In particular, cloud-native operators require a holistic view of the dynamic security posture for the microservice-based cloud-native environment from a defense aspect. Additionally, both attackers and defenders can adopt advanced AI technologies. This makes the dynamic interaction and benchmark among different intelligent offense and defense strategies more crucial. Hence, following the multi-agent deep reinforcement learning (RL) paradigm, this research develops an agent-based intelligent security service framework (ISSF) for cloud-native operations. It includes a dynamic attack graph model to represent the cloud-native environment and an action model to represent offense and defense actions. Then we develop an approach to enable the training, publishing, and evaluating of intelligent security services using diverse deep RL algorithms and training strategies, facilitating their systematic development and benchmarking. The experiments demonstrate that our framework can sufficiently model the security posture of a cloudnative system for defenders, effectively develop and quantitatively benchmark different intelligent security services for both attackers and defenders, and guide further optimization.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"655-686"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foundational Components for B2B Data Sharing Using the Solid Protocol","authors":"Andreas Both;Thorsten Kastner;Dustin Yeboah;Christoph Braun;Daniel Schraudner;Sebastian Schmid;Tobias Käfer;Andreas Harth","doi":"10.13052/jwe1540-9589.2445","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2445","url":null,"abstract":"This article introduces foundational components for decentralized B2B data sharing based on the solid protocol, emphasizing data sovereignty, security, and interoperability. These components are: (1) Authorization app (AuthApp) – facilitating granular control and compliance in access granting and revocation processes; (2) rights delegation proxy (RDP) – supporting controlled delegation of rights, enabling natural persons to act on behalf of organizations while ensuring privacy and traceability; (3) data provisioning proxy (DPP) – allowing seamless and secure data provisioning across organizations while masking the identity of upstream data sources to protect business interests. The components enable the creation of end-to-end, standards-based, flexible data value chains. We validate their applicability through a real-world financial services use case involving loan processing, which illustrates data sharing and protection challenges in B2B ecosystems.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"593-634"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book","authors":"Youngmo Kim;Seok-Yoon Kim;Byeongchan Park","doi":"10.13052/jwe1540-9589.2422","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2422","url":null,"abstract":"In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user's recent interest without considering the changes in preference over time, and the user's satisfaction decreases because it repeatedly recommends only similar items. In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users' past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users' recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 2","pages":"199-216"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fort2BCK: Fortifying Signatures in Healthcare Environments Through Blockchain","authors":"Cinthia Paola Pascual Caceres;José Vicente Berná Martínez;María Esther Almaral Martínez;Lucía Arnau Muñoz","doi":"10.13052/jwe1540-9589.2433","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2433","url":null,"abstract":"This study introduces Fort2BCK, an advanced security framework designed to mitigate critical vulnerabilities in healthcare blockchain implementation, specifically data manipulation, unauthorised access and weaknesses in consensus protocols. Fort2BCK employs a dual verification mechanism, combining native consensus algorithm validation with the application of advanced cryptographic signatures (RSA, ECDSA and zero knowledge proofs, ZKPs), thus providing an additional layer of authentication, auditing and resistance to malicious attacks. In contrast to traditional approaches, Fort2BCK significantly reduces the risks of fraud and forgery by independently cryptographically verifying each block before it is integrated into the blockchain, strengthening security in scenarios where conventional consensus models may be vulnerable. In addition, its interoperability with multiple blockchain architectures, including proof of work (PoW), proof of stake (PoS) and delegated proof of stake (DPoS), allows it to effectively mitigate attacks such as the 51% attack in PoW and the nothing-at-stake problem in PoS, through an integrated external validation layer. To evaluate the effectiveness of Fort2BCK, experiments were conducted on a simulated hybrid blockchain network with 100 nodes and 50,000 transactions. The results revealed that Fort2BCK increases security by 35% against block rewrite attacks and decreases the rate of fraudulent transactions by 42%, compared to conventional blockchain systems, while maintaining a computational overhead of less than 8%. Additionally, Fort2BCK ensures compliance with regulations such as HIPAA and GDPR, ensuring that blockchain systems for the healthcare sector meet legal and privacy requirements. These findings demonstrate that Fort2BCK optimises the security, scalability and privacy of medical blockchains, facilitating the secure digitisation of healthcare systems and strengthening trust in clinical data management.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 3","pages":"383-408"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Deep Learning and Feature Aggregation Techniques for Web Security","authors":"Jinxin Wang","doi":"10.13052/jwe1540-9589.2426","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2426","url":null,"abstract":"With the rapid development of internet technologies, Web services have been widely applied in various fields, including finance, healthcare, education, ecommerce, and the Internet of Things, bringing great convenience to humanity. However, Web security threats have become increasingly severe, with side-channel attacks (SCA) emerging as a covert and highly dangerous attack method. SCAs exploit non-explicit information, such as network traffic patterns and response times, to steal sensitive user data, posing serious threats to user privacy and system security. Traditional detection methods primarily rely on rule-based feature engineering and statistical analysis, but these methods show significant limitations in terms of detection performance when dealing with complex attack patterns and high-dimensional, large-scale network traffic data. To address these issues, this paper proposes a side-channel leakage detection method based on SSA-ResNet-SAN. The SSA (sparrow search algorithm) is an optimization mechanism, intelligently searching for globally optimal feature subsets to enhance the model's feature selection capabilities and global optimization performance. Combined with deep residual networks (ResNet) and the signature aggregation network (SAN), the method performs a comprehensive analysis of both single-attribute and aggregated-attribute features in network traffic, thereby improving the model's accuracy and robustness. Experimental results demonstrate that SSA-ResNet-SAN significantly outperforms existing methods on multiple practical datasets. On the Google dataset, the use of aggregated attribute features enables SSA-ResNet-SAN to achieve an accuracy of 93%, which is substantially higher than that of other models. Furthermore, in multi-class tasks on the Baidu and Bing datasets, SSA-ResNet-SAN exhibits strong robustness and applicability. These experimental results fully validate the outstanding performance of SSA-ResNet-SAN in side-channel leakage detection, providing an efficient and reliable solution for the field of Web security.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 2","pages":"291-316"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Vulnerability Detection Method for Internet Cross-Site Scripting Based on Relationship Diagram Convolutional Networks","authors":"Zhida Guo;Xiaoli Li;Ran Hu;Dapeng Wang;Weijie Song","doi":"10.13052/jwe1540-9589.2424","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2424","url":null,"abstract":"The aim of this research is to quickly detect cross-site scripting (XSS) attacks on the internet based on relationship diagram convolutional networks. Based on the principle and attack process of cross-site scripting attacks, domain knowledge is used to build an XSS ontology to conduct high-level modeling of cross-site scripting attacks, obtain data that can reflect XSS attacks, normalize these attack data, extract attack data word vectors, use them as the input of the relationship diagram convolution networks added to the attention mechanism, and learn attack feature word vectors. After further extracting node characteristics through convolution and pooling, all node characteristics are aggregated and fed into the fully connected neural network. XSS vulnerability detection results are obtained through classification of the activation function, and malicious domain name and malicious IP information are combined as supplementary rules to improve the effectiveness of the vulnerability detection in internet cross-site scripting based on the relationship graph convolution network. Experiments show that this method can accurately detect XSS vulnerabilities, provide comprehensive and accurate attack details, and its performance is better than that of the literature method, which is reflected in the higher accuracy, recall, accuracy and F1 value, and the leading area of the ROC curve. Its detection speed is extremely fast, only 0.03 s, and by combining malicious domain name and IP information, the detection efficiency is further improved, realizing rapid response and effectively maintaining Internet security.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 2","pages":"243-266"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Framework for Blockchain-Based Secure Management of Mobile Healthcare (mHealth) Systems","authors":"Adel Alkhalil;Abdul Razzaq;Aakash Ahmad;Magdy Abdelrhman;Yaser Altameemi;Mohammed Altamimi;Zhang Tao","doi":"10.13052/jwe1540-9589.2431","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2431","url":null,"abstract":"In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algorithms, and experimental evaluation – aims to advance state-of-the-art architecting and implementing cybersecurity mHealth solutions using blockchain technology.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 3","pages":"317-354"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmenting Websites with Voice Commands: An Approach Focused on Accessibility","authors":"César González-Mora;Irene Garrigós;Sven Casteleyn;Sergio Firmenich","doi":"10.13052/jwe1540-9589.2421","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2421","url":null,"abstract":"Even now, users with disabilities encounter serious barriers when accessing the Web. In particular, blind and visually impaired users encounter difficulties browsing and reading the contents of a website. Screen readers provide some assistance, yet, as they are unable to interpret the Web structure, they summarise information and read specific labelled fragments. Therefore, the overall comprehension of the text remains challenging. In this sense, in order to improve the accessibility of websites on the fly, we propose a Web augmentation framework for accessibility (WAFRA). Our framework uses Web augmentation techniques that extend the website with voice interaction and new actions: label text fragments, read aloud these fragments, facilitate navigation, increase font size and show videos. In order to perform this accessibility improvement, we automatically provide annotations from DBPedia regarding important information for end users. Moreover, we also provide the option that intermediary users add new annotations for labelling or including more specific information, which can be shared with other users by crowdsourcing. The evaluation of the framework shows its usefulness to ease website access for users with visual disabilities compared to using screen readers.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 2","pages":"163-198"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Web Traffic Modelling and Forecasting with a Hybrid Predictive Approach","authors":"Ujjwal Thakur;Sunil K. Singh;Sudhakar Kumar;Harmanjot Singh;Varsha Arya;Brij B. Gupta;Razaz Waheeb Attar;Ahmed Alhomoud;Kwok Tai Chui","doi":"10.13052/jwe1540-9589.2434","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2434","url":null,"abstract":"Web traffic analysis is crucial for optimising user experience and engagement. This research explores a hybrid approach combining traditional statistical methods, like the autoregressive integrated moving average (ARIMA) model, with advanced techniques such as long short-term memory (LSTM) neural networks and the Prophet model. ARIMA effectively captures linear trends, seasonal effects, and cyclic behaviours, while LSTM handles complex non-linear patterns, and Prophet addresses seasonal variations and missing data. The hybrid model demonstrated 93% accuracy in predicting web traffic, highlighting the benefits of integrating these methodologies. This approach enables businesses to better manage resources, boost user engagement, and improve revenue. Future research will focus on refining hybrid models by incorporating new data features and ensemble methods to further enhance prediction accuracy, ultimately advancing the understanding of web traffic trends and user behaviour.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 3","pages":"409-456"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037626","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}