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":"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":"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":"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":"The Potential of Serverless Edge-Powered Islands for Web Development","authors":"Juho Vepsäläinen;Petri Vuorimaa;Arto Hellas","doi":"10.13052/jwe1540-9589.2411","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2411","url":null,"abstract":"Web developers face two significant challenges when developing their applications and websites: latency and payload size. Given that web services rely on servers, the related communication incurs a cost in terms of latency. In contrast, the payload passed to the client incurs a communication cost, not to mention the computational cost to the client. The concept of serverless edge computing, built on top of content delivery networks (CDNs), is an approach that has begun to gain the attention of web developers for its promise of lower latencies due to its efficiencies in communication thanks to globally distributed networks and replication. Islands architecture is a technical approach that addresses payload size by giving developers easy ways to defer and potentially even avoid the cost of loading content. Combined, these two approaches form edge-powered islands and, in this article, we examine how the combination can help to address these two notable costs web developers have to consider in their daily work. Our findings indicate that edge-powered islands can provide a way to introduce interactivity to otherwise static websites while wrapping dynamic portions of a page within islands to gain the benefits of static approaches in more dynamic contexts, such as storefronts. In addition, islands can provide loading benefits even for more application-like websites, such as social networks, and give web developers an additional control layer in their development work.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"1-38"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924701","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602042","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 User Behavior Prediction Method for Web Applications Based on Deep Forest","authors":"Chang-Sheng Ma;Xiang-Ran Du;Jing Lou;Ming-Qian Wang","doi":"10.13052/jwe1540-9589.2412","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2412","url":null,"abstract":"To increase the sales of agricultural products in e-commerce, understanding customer preferences is essential. In agricultural web applications, data mining techniques can help farmers analyze customer behavior patterns and identify preferences, thus optimizing product design or offering more precise personalized services, which, in turn, can enhance farmers' decision-making in agricultural production. This study proposes a web application user behavior prediction method based on deep forest, which addresses the issue of traditional learning methods requiring a large number of hyperparameter settings. Analysis results show that the Mondrian deep forest model has an accuracy of 95.42% and a running time of 55 s. The accuracy and efficiency of the Mondrian deep forest model are higher than for other models, and the proposed model can improve the accuracy of predicting user behavior in web applications. The effectiveness of the algorithm has been validated through practical testing.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"39-56"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602017","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":"Hybrid Top Features Extraction Model for Detecting X Rumor Events Using an Ensemble Method","authors":"Taukir Alam;Wei Chung Shia;Fang Rong Hsu;Taimoor Hassan;Pei-Chun Lin;Eric Odle;Junzo Watada","doi":"10.13052/jwe1540-9589.2414","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2414","url":null,"abstract":"The paper describes a novel a hybrid ensemble algorithm (HEA) that combines ensemble learning, class imbalance handling, and feature extraction. To address class imbalance in the dataset, the suggested approach integrates SMOTE oversampling and random under sampling (RU) feature extraction. To begin, Pearson correlation analysis is used to detect highly associated features in a dataset. This analysis aids in the selection of the most relevant features, which are either substantially related to the target variable or have a strong association with other features. The method seeks to improve classification performance by focusing on these correlated features. Following that, the SMOTE oversampling and RU algorithms are used to balance the majority and minority categorization characteristics. The SMOTE (synthetic minority oversampling technique) develops synthetic cases for the minority class by interpolating between existing instances, enhancing minority class representation. RU, on the other hand, removes instances from the majority class at random to obtain a balanced distribution. Furthermore, the random forest classifier (RFC) model's key features are input into an ensemble of decision tree (DT), k-nearest neighbor (KNN), adaptive boosting (AdaBoost), and convolutional neural network (CNN) approaches. This ensemble approach combines multiple models' predictions, exploiting their particular strengths and catching varied patterns in the data. Popular machine learning algorithms include DT, KNN, AdaBoost, and CNN, which are notable for their capacity to handle many types of data and capture complicated relationships. The evaluation findings show that the suggested HEA approach is effective, with a maximum precision, recall, F-score, and accuracy of 90%. The proposed methodology produces encouraging results, proving its applicability to a variety of categorization problems.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"79-106"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602018","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":"Enhancing Collaborative Filtering with Game Theory for Educational Recommendations: The Edu-CF-GT Approach","authors":"Rezoug Nachida;Selma Benkessirat;Fatima Boumahdi","doi":"10.13052/jwe1540-9589.2413","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2413","url":null,"abstract":"In the field of education, the proliferation of e-learning platforms has considerably increased access to teaching material. However, this abundance of resources poses a serious challenge to learners in the form of information overload that hinders the learning process. To meet this challenge, effective mechanisms need to be put in place to guide learners towards resources that are tailored to their individual needs and preferences. Recommendation systems appear to be essential tools in this context, aiming to personalise the learning experience by offering targeted suggestions based on the user's preferences. This article presents EDU-CF-GT, a new educational recommendation model, as a solution to this challenge. Based on our generic CF-GT model, EDU-CF-GT is adapted to the complexities of the educational domain, improving learning efficiency by simplifying access to resources. Through evaluation on an educational dataset, EDU-CF-GT demonstrates significant improvements in recommendation relevance and learner satisfaction compared to existing models.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"57-78"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601929","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}