Journal of Web Engineering最新文献

{"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}

{"title":"Automatic Detection Method of Website Vulnerabilities Based on an Associated Data Drive","authors":"Xiaoli Li;Ling Zhao;Haobin Shen;Hanlin Du;Zhida Guo","doi":"10.13052/jwe1540-9589.2423","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2423","url":null,"abstract":"In order to reduce the probability of website users being attacked and maintain the safety of website operation, this study proposes an automatic vulnerability detection method of websites based on associated data. We use plug-ins to scan the website in all directions, establish a scanning database, and classify and store the scanned web data. By applying optimized an a priori association rule algorithm, key features are extracted from web scan data, which are then transformed into input samples for a K-means clustering algorithm. The aim is to efficiently extract feature attributes of website vulnerability data and ultimately construct a text vectorized representation of vulnerability data. Convolutional neural networks can automatically detect website vulnerabilities by using the constructed text vector as input. Experimental verification shows that this method demonstrates comprehensive data coverage, efficient processing speed, and high-precision recognition performance. It not only significantly reduces the clustering analysis time, but also ensures the accuracy and timeliness of vulnerability detection.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 2","pages":"217-242"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888382","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":"Design of a Web Content Personalized Recommendation System Based on Collaborative Filtering Improved by Combining k-Means and LightGBM","authors":"Xiaoming Li","doi":"10.13052/jwe1540-9589.2425","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2425","url":null,"abstract":"To improve the precision of Web content personalized recommendation, a Web content personalized recommendation system based on collaborative filtering improved by combining k-means and LightGBM is proposed. Firstly, the k-means clustering algorithm (k-means) is improved by using the Rat Swarm Optimizer (RSO) algorithm to cluster and group users and Web content. At the same time, Light Gradient Boosting Machine (LightGBM) algorithm is introduced to predict the level of interest of users in web content, and collaborative filtering recommendation method improved by combining k-means and LightGBM is proposed. Then, simulation experiments are conducted, thus verifying the recommendation method. Finally, B/S architecture is used to design and test the recommendation system. The results reveal that MAE and RMSE of the collaborative filtering recommendation method is improved by combining k-means and LightGBM for recommendation on the UserBehavior dataset are 1.08% and 2.41%, respectively, and its precision, recall and F1 are 98.76%, 98.64% and 98.53%, respectively. Therefore, a Web content personalized recommendation system based on collaborative filtering improved by combining k-means and LightGBM has perfect functional modules, and it can meet Web content personalized recommendation, which has certain practical application value.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 2","pages":"267-290"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888429","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 Hybrid Security Framework for Web Applications Using Blockchain and Adaptive Adversarial Learning","authors":"Han Wu;Shugong Zhou","doi":"10.13052/jwe1540-9589.2432","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2432","url":null,"abstract":"Web applications are increasingly vulnerable to sophisticated cyberattacks, and traditional security methods often fail to address the dynamic nature of modern threats. To tackle these challenges, we propose a novel security model that integrates blockchain technology, deep learning, and adaptive adversarial learning (ARL). This model aims to enhance web application security by ensuring data integrity, enabling intelligent attack detection, and optimizing defense strategies in real time. By combining these advanced technologies, our model offers a scalable and adaptive solution capable of defending against both known and unknown attacks. Experimental results demonstrate that our approach outperforms existing methods, providing superior protection and resilience against a wide range of cyber threats. Our model not only improves detection accuracy but also significantly enhances response times and overall defense efficiency. These results highlight the effectiveness of the proposed model in providing robust and efficient protection for web applications, offering significant improvements over traditional methods in handling dynamic and evolving cyber threats.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 3","pages":"355-382"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299238","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}