Journal of Web Engineering最新文献_第8页

Design of a Web Content Personalized Recommendation System Based on Collaborative Filtering Improved by Combining k-Means and LightGBM 结合k-Means和LightGBM改进的协同过滤Web内容个性化推荐系统设计

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-03-01 DOI: 10.13052/jwe1540-9589.2425

Xiaoming Li

{"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}

引用次数: 0

A Hybrid Security Framework for Web Applications Using Blockchain and Adaptive Adversarial Learning 使用区块链和自适应对抗学习的Web应用混合安全框架

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-03-01 DOI: 10.13052/jwe1540-9589.2432

Han Wu;Shugong Zhou

{"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}

引用次数: 0

A Keyword-Based IP Tracking Method for Illegal Web Content Distribution Using Port Scanning on HTTP and HTTPS 基于HTTP和HTTPS端口扫描的基于关键字的非法Web内容分发IP跟踪方法

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-03-01 DOI: 10.13052/jwe1540-9589.2435

Seyoung Jang;Byeongchan Park;Seok-Yoon Kim;Youngmo Kim

{"title":"A Keyword-Based IP Tracking Method for Illegal Web Content Distribution Using Port Scanning on HTTP and HTTPS","authors":"Seyoung Jang;Byeongchan Park;Seok-Yoon Kim;Youngmo Kim","doi":"10.13052/jwe1540-9589.2435","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2435","url":null,"abstract":"The rapid expansion of online content distribution has led to a significant increase in copyright infringement, where unauthorized works are illegally shared through various web-based platforms. To fundamentally block these copyright-infringing websites, it is essential to accurately identify the IP address or physical location of the original server. However, most illegal content distribution sites utilize advanced security mechanisms, such as DNS resolvers, reverse proxies, and anonymization techniques, to conceal their true IP addresses, making direct tracking increasingly difficult. These evasive tactics allow illegal sites to continue operating while avoiding enforcement measures. To address this challenge, this paper proposes a keyword-based IP tracking method for identifying illegal web content distribution sites by leveraging port scanning on HTTP and HTTPS (ports 80 and 443). The proposed approach systematically detects and analyzes servers that provide unauthorized content by scanning network ports commonly used for web services. By correlating detected IP addresses with keyword-based filtering techniques, this method enables efficient tracking of illegal sites that actively hide their original server's IP address. Through experimental validation, the proposed method successfully pinpoints the IP addresses of illegal content distribution servers, even when they employ obfuscation techniques to mask their identity. This study contributes to enhancing copyright protection by introducing a web-based detection approach that integrates network security techniques, web engineering principles, and automated keyword analysis. Furthermore, the findings provide a practical solution for law enforcement agencies, copyright holders, and regulatory bodies to combat illegal web content distribution more effectively.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 3","pages":"457-472"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299099","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}

引用次数: 0

Automatic Detection Method of Website Vulnerabilities Based on an Associated Data Drive 基于关联数据驱动器的网站漏洞自动检测方法

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-03-01 DOI: 10.13052/jwe1540-9589.2423

Xiaoli Li;Ling Zhao;Haobin Shen;Hanlin Du;Zhida Guo

{"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}

引用次数: 0

The Potential of Serverless Edge-Powered Islands for Web Development 无服务器边缘驱动孤岛在Web开发中的潜力

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-01-01 DOI: 10.13052/jwe1540-9589.2411

Juho Vepsäläinen;Petri Vuorimaa;Arto Hellas

{"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}

引用次数: 0

A User Behavior Prediction Method for Web Applications Based on Deep Forest 基于深度森林的Web应用用户行为预测方法

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-01-01 DOI: 10.13052/jwe1540-9589.2412

Chang-Sheng Ma;Xiang-Ran Du;Jing Lou;Ming-Qian Wang

引用次数: 0

Hybrid Top Features Extraction Model for Detecting X Rumor Events Using an Ensemble Method 基于集成方法的混合顶级特征提取模型用于检测X个谣言事件

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-01-01 DOI: 10.13052/jwe1540-9589.2414

Taukir Alam;Wei Chung Shia;Fang Rong Hsu;Taimoor Hassan;Pei-Chun Lin;Eric Odle;Junzo Watada

{"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}

引用次数: 0

Enhancing Collaborative Filtering with Game Theory for Educational Recommendations: The Edu-CF-GT Approach 基于博弈论的教育推荐协同过滤：Edu-CF-GT方法

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-01-01 DOI: 10.13052/jwe1540-9589.2413

Rezoug Nachida;Selma Benkessirat;Fatima Boumahdi

{"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}

引用次数: 0

Generative AI-Driven Graphic Pipeline for Web-Based Editing of 4D Volumetric Data 生成式ai驱动的基于web的四维体数据编辑图形管道

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-01-01 DOI: 10.13052/jwe1540-9589.2416

Ye-Won Jang;Jung-Woo Kim;Hak-Bum Lee;Young-Ho Seo

{"title":"Generative AI-Driven Graphic Pipeline for Web-Based Editing of 4D Volumetric Data","authors":"Ye-Won Jang;Jung-Woo Kim;Hak-Bum Lee;Young-Ho Seo","doi":"10.13052/jwe1540-9589.2416","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2416","url":null,"abstract":"This paper proposes a novel approach to adding and editing clothing and movement of 4D volumetric video data in a web-based environment. While significant advancements have been made in 3D modeling and animation, efficiently editing 3D mesh data produced in sequence remains a challenging problem. Since 3D mesh data synthesized from multiple cameras exists continuously over time, modifying a single 3D mesh model requires consistent editing across multiple frames. Most existing methods focus on single meshes or static 3D models, limiting their ability to handle the complexity of timevarying 3D mesh sequences. The method proposed in this paper targets 3D volumetric sequences synthesized from multiple cameras. It utilizes deep learning networks to estimate body poses, facial features, and hand shapes from RGB images, generating 3D models using the SMPL-X method. Subsequently, an algorithm is applied to segment the 3D mesh, separating and combining the head and torso of the model to create a new 3D model. In the web-based environment, this process makes the data editable, allowing for adding new motions or replacing clothing, which can be seamlessly composited into the existing sequence video. The proposed method enables editing and modification of various types of 3D mesh sequences, facilitating enhancements to existing sequences, such as changing the motion of characters or replacing their clothing, thereby improving the overall quality of 3D content creation in online applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"135-162"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601939","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}

引用次数: 0

Mesh Enhancement of a 3D Volumetric Model Using Generative AI for a Web 3.0-Based Graphic Service 基于Web 3.0的图形服务使用生成式AI的3D体积模型网格增强

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-01-01 DOI: 10.13052/jwe1540-9589.2415

Byung-Seo Park;Ye-Won Jang;Hak-Bum Lee;Young-Ho Seo

引用次数: 0