Journal of Web Engineering最新文献

{"title":"A Multimodal Threat Detection Algorithm for Wide Area Network Security Based on Support Vector Machines","authors":"Bo Yuan","doi":"10.13052/jwe1540-9589.2465","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2465","url":null,"abstract":"Wide area networks (WANs) are increasingly susceptible to sophisticated cyber threats, particularly as critical infrastructure becomes more interconnected. For example, computing-first networks (CFNs) often traverse WANs at edge access nodes, making them more vulnerable to security threats. This paper proposes a multimodal threat detection framework that combines traffic statistics, system logs, and user behavior patterns to deliver interpretable and real-time classification of network threats. The system applies feature normalization and uses principal component analysis (PCA) to reduce dimensionality. A support vector machine (SVM) with a radial basis function kernel is then used to detect non-linear attack patterns. A web-based architecture enables real-time deployment via REST APIs, and extensive evaluations on the CICIDS 2017 and UNSW-NB15 datasets demonstrate high accuracy (up to 96.8%) and low-latency inference. Ablation studies confirm the importance of multimodal fusion, and benchmark tests validate scalability and system responsiveness. This work offers a deployable and efficient solution for real-time WAN security, with promising applications in energy systems, public infrastructure, and enterprise networks.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 6","pages":"973-996"},"PeriodicalIF":1.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11194294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230092","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 Web Application Framework for Battery Health Prediction in Industrial IoT Networks","authors":"Seongseop Kim;Seungwoo Lee;Minsu Kim;Youngmin Kwon","doi":"10.13052/jwe1540-9589.2464","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2464","url":null,"abstract":"This study presents a web engineering architecture for predictive battery health management in industrial IoT environments. The proposed framework leverages a scalable web-based platform that integrates data streams, web services, and machine learning modules to estimate the state of charge (SOC) of primary lithium batteries. These batteries are critical for long-term device reliability in applications such as gas advanced metering infrastructure (AMI) networks. To overcome challenges associated with flat discharge profiles and data sparsity, the framework incorporates web-enabled data processing, online augmentation techniques (e.g., CutMix), and adaptive learning models. A key contribution of this work is the design of a modular web application layer compliant with oneM2M standards and RESTful interfaces. It includes components for real-time monitoring, automated model updates, and secure service orchestration using technologies such as HTTP bindings. This architecture not only enables accurate SOC estimation without additional hardware but also demonstrates the critical role of web engineering in ensuring system scalability, security, and integration across heterogeneous IoT devices. Experimental validation in AMI systems confirms the effectiveness of the approach, which is extensible to broader domains such as smart utilities, environmental sensing, and industrial automation.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 6","pages":"943-972"},"PeriodicalIF":1.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11194296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230090","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":"Lightweight Test-Time Adaptation for Robust Out-of-Distribution Face Recognition in Web Services","authors":"Dongyoon Seo;Taebeom Lee;Jeongyoon Yoon;Chiho Park;Sangpil Kim;Miyoung Kim;Byoungsoo Koh","doi":"10.13052/jwe1540-9589.2462","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2462","url":null,"abstract":"Face recognition systems have the potential to support diverse services in Web 3.0 applications, yet two critical challenges remain underexplored. First, existing benchmark datasets are demographically biased and underrepresent elderly East Asian users, limiting fair and inclusive deployment. Second, sensor noise, lighting shifts, and motion blur introduce out-of-distribution (OOD) corruptions that cause severe accuracy degradation and undermine reliability in decentralized environments. To address these issues, we introduce the Korean Senior Face Benchmark, consisting of 700 images of 70 Korean senior celebrities, enabling realistic assessment for an underrepresented demographic. We quantitatively demonstrate that recent state-of-the-art models suffer significant performance drops under realistic corruption conditions, highlighting the need for enhanced robustness. Finally, we show that a lightweight test-time adaptation (TTA) strategy can recover OOD performance without retraining, making it well-suited for edge devices and distributed infrastructures while preserving user privacy. Experiments show accuracy gains of up to 41.5% under the most severe corruptions, along with improvements in intra-class compactness and inter-class separability in the embedding space. The proposed benchmark and adaptation pipeline lay a practical foundation for building distributed, fair, and privacy-aware face-recognition services in Web 3.0 applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 6","pages":"871-910"},"PeriodicalIF":1.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11194307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230093","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 Web-Based Identification Method for Illegal Streaming Videos Using Low-Frequency Components of the Fast Fourier Transform","authors":"Injae Yoo;Byeongchan Park;Seok-Yoon Kim;Youngmo Kim","doi":"10.13052/jwe1540-9589.2461","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2461","url":null,"abstract":"With the proliferation of web-based content platforms, the distribution of illegally streamed videos poses a serious threat to the reliability of web applications and the integrity of content copyright protection systems. Traditional video identification methods typically require the processing of large-scale feature data, which hinders the real-time performance, lightweight nature, and scalability demanded by web environments. In this paper, we propose a method for identifying illegally streamed videos that is optimized for efficient operation within web systems. The proposed approach utilizes only the low-frequency components of the fast Fourier transform (FFT). By transforming video frames into the frequency domain and extracting the structurally significant low-frequency components, the method replaces high-dimensional feature data with more compact representations. This allows the system to maintain low computational complexity and fast response times, even in web application environments. Experimental results demonstrate that, compared to existing methods, the proposed technique achieves up to 93 times reduction in feature data size, a recognition rate of 98%, and an average response time of 1745 ms. From the perspective of web engineering, the proposed method holds strong potential as a real-time identification module in web-based copyright protection systems. It offers a balanced approach that satisfies both lightweight processing requirements and high accuracy.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 6","pages":"851-870"},"PeriodicalIF":1.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11194299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230091","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 Digital Grid Security Architecture Based on Quantum Key Interaction and Web Engineering for Distributed Energy Systems","authors":"Yiming Zhang;Ziyang Yang;Xinglong Liu","doi":"10.13052/jwe1540-9589.2466","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2466","url":null,"abstract":"The modernization of distributed energy systems introduces complex cyber-security challenges as grid infrastructures become increasingly digitized, decentralized, and web-connected. This paper presents a novel security architecture that integrates quantum key distribution (QKD) with semantic web technologies to provide end-to-end secure, scalable, and adaptive protection for distributed energy resource (DER) networks. The proposed framework features a modular system design, incorporating BB84-based QKD protocols for quantum-resilient key generation, a metadata-driven policy layer using OWL ontologies and SWRL reasoning, and a web interface for operator access and real-time monitoring. Extensive performance evaluation in a simulated multi-domain microgrid environment demonstrates that the system achieves an average key generation rate of 2.3 kbps with quantum bit error rate (QBER) maintained below 5.2% across 40 km optical links. Session establishment latency averaged 435 ms, 29.8% lower than a traditional TLS/PKI baseline, while semantic access validation achieved 100% accuracy in 42 adversarial test cases. These cases were evaluated using automated semantic validation scripts simulating spoofed roles, malformed sessions, and unauthorized requests. The system sustained encrypted throughput of 110 messages per second per node and maintained service continuity under quantum noise and cross-domain attack simulations. Usability trials with six engineers yielded a system usability scale (SUS) score of 88.3, and the average DER onboarding time was reduced from 10.1 to 5.5 minutes. These findings affirm that QKD-enhanced, semantically governed web architectures can provide strong cryptographic guarantees while supporting dynamic policy enforcement and intuitive user workflows. The proposed solution demonstrates a viable path for deploying future-proof security mechanisms in next-generation smart grid environments.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 6","pages":"997-1022"},"PeriodicalIF":1.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11194300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230088","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":"Discovery of Modularity in Monolithic Java Project Codes Using Complex Networks","authors":"Marcos Cordeiro de Brito;Calebe P. Bianchini;Leandro A. Silva","doi":"10.13052/jwe1540-9589.2463","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2463","url":null,"abstract":"Monolithic architecture is a software design which brings significant difficulties to system developers when it comes to maintenance or expanding the scope of a project. On the other hand, a modular project consists of several similar entities, or modules, which are the object of similar functions or processes that, applied repeatedly, have well-defined classes and smaller modules to work, bringing benefits such as reduced project development time and increased productivity for the system developers. This work proposes the use of complex networks through the NetworkX library in Python, using modularity detection algorithms for the static analysis of Java code. The goal is to discover modules by analyzing dependencies between classes, indicating the best way to identify code clusters to be treated as modules automatically. The outcomes of applying the Greedy Modularity, Louvain, K-Clique, and Girvan Newman algorithms to two open-source projects will be presented. A comparative analysis of these results will be illustrated using generated graphs and a distribution map, emphasizing the number of communities identified by each algorithm.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 6","pages":"911-942"},"PeriodicalIF":1.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11194305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230095","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":"Robust Cloud Service Ranking with Deep Learning and Multi-Criteria Analysis","authors":"Pooja Goyal;Sukhvinder Singh Deora","doi":"10.13052/jwe1540-9589.2453","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2453","url":null,"abstract":"With the rapid growth of cloud services, it is crucial to have strong assessment methods in place to rate these services according to their performance, dependability, and security. This study introduces a holistic methodology that utilizes advanced deep learning (DL) algorithms to prioritize and evaluate cloud services. Our model incorporates many assessment criteria, including latency, throughput, availability, and security measures. These criteria are trained using a varied collection of performance measurements from cloud services. We validate the effectiveness of our methodology by comprehensive experiments, attaining greater precision and significance in ranking compared to conventional approaches. The DL model underwent evaluation using a testing set, resulting in a mean absolute error (MAE) of 0.15 in ranking scores. The algorithm regularly achieved superior results compared to conventional ranking approaches, particularly in situations where performance measures varied. Through the incorporation of security metrics, the model successfully assessed and ranked cloud service providers (CSPs) based not only on their performance, but also on their ability to withstand security threats. The DL technique exhibited more flexibility and contextual awareness in its rankings, hence showcasing its superiority in adjusting to real-time data. The research conducted a comparison between DL-based rankings and conventional methodologies and industry standards, demonstrating its superiority in effectively adjusting to real-time data. The study technique entails gathering data from many CSPs to construct a resilient framework for evaluating cloud services using DL models. The data is obtained from publicly available performance statistics, cloud monitoring tools, user evaluations, and problem reports. The collection comprises both structured and unstructured data, including essential performance and accuracy indicators.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 5","pages":"739-772"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11135461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896808","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":"Prediction and Performance Optimization of Hospital Web Application Access Modes Based on Big Data Analysis","authors":"Xiaoye Zhang;Sen Wang","doi":"10.13052/jwe1540-9589.2456","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2456","url":null,"abstract":"Data in hospital information systems contain errors, inconsistencies, and other issues that mask the true underlying patterns in the data, making the existing time series patterns blurry and leading to increased prediction errors in access patterns. To this end, research is being conducted on the prediction and performance optimization of hospital web application access patterns based on big data analysis. Firstly, semi-structured log data and structured information data from hospital web applications are collected and preprocessed. Then, a deep belief network (DBN) is used for feature extraction, and a deep learning model consisting of a stacked restricted Boltzmann machine (RBM) and a BP neural network is utilized to automatically extract multidimensional information such as user behavior features, temporal features, and page features, constructing a comprehensive feature system. Finally, based on the gated recurrent unit (GRU) neural network for access mode prediction, the control information selection mechanism of GRU is utilized to capture time series information and improve the accuracy of prediction. The experimental results show that the proposed prediction method has a practical application value with a mean square error of 0–50 for predicting traffic and a response rate within 5 seconds after application.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 5","pages":"827-850"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11135459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896809","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":"Web Crawling Algorithm Fusing TF-IDF and Word2Vec Feature Extraction","authors":"Xinyue Feng","doi":"10.13052/jwe1540-9589.2452","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2452","url":null,"abstract":"Current research focuses on how to efficiently extract and crawl network information because, with the growth of the Internet, network information is becoming more and more diverse. To address the problem of incorrect data extraction and topic judgment of web crawlers, this study proposes a novel approach based on a file inverse frequency algorithm and Word2Vec feature extraction. The new method improves the retrieval capability of web crawlers by using the file inverse frequency algorithm and uses Word2Vec to extract data features, which improves the data extraction capability of current crawlers. The results showed that the F1 values of the research use model were 25.8% and 26.2% higher than those of the digital filtering algorithm, respectively. The total number of localization resources for the research use strategy was 2800 and the network coverage was 81%, which was 12% higher than the optimal strategy. The research use strategy had a shorter retrieval time and the model could recognize the vocabulary of the keywords. Finally, the model used by the research also had a good model processing capability when compared to other models. In summary, the new model built by the research can improve the data retrieval ability and data extraction ability of the web crawler, which provides new research ideas for future web information extraction.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 5","pages":"713-738"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11135464","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896763","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 the Application of Graph Neural Networks Based on Multiple Attention Mechanisms in Personalized Recommendation","authors":"Lingling Kong;Hongyan Deng;Jiayi Huang","doi":"10.13052/jwe1540-9589.2454","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2454","url":null,"abstract":"Graph neural networks (GNNs) have been widely applied due to their ability to model interactions among different objects. However, from the perspective of mathematical graph theory, the existing GNN frameworks still face challenges when dealing with specific graph structure problems. Nevertheless, the existing graph neural networks are unable to accurately identify and capture user characteristics based on common interests, have difficulty in flexibly handling diverse user interests and the differences in interests among users, and cannot effectively extract and utilize the feature information of intermediate nodes. To address these issues, this paper proposes a heterogeneous graph recommendation model based on a multi-level attention mechanism (MAHGRM-HGNN). The MAHGRM model consists of three major modules: the node-level aggregation and feature fusion module, the semantic-level aggregation module, and the importance analysis module. By introducing dual attention mechanisms at the node level and semantic level, MAHGRM can effectively identify and fuse multi-hop neighbor information related to user interests, while modeling the semantic features represented by different paths. Additionally, MAHGRM adopts an innovative feature fusion method, integrating intermediate heterogeneous nodes and their related different paths according to the topological structure of the graph, thereby avoiding the loss of intermediate node information and enriching the feature representation of the target node. In the importance analysis module, MAHGRM introduces a strategy for evaluating the importance of product nodes by calculating the importance scores of different product nodes, selecting the most popular products as the candidate set, and randomly selecting some products from it for recommendation to users. MAHGRM combines the node-level aggregation and feature fusion, semantic-level aggregation, and importance analysis modules closely. The key advantage lies in its ability to effectively integrate multi-level information in heterogeneous graphs and the collaborative optimization effect brought by the cross-module feature sharing mechanism, making the final recommendation results more targeted and timely. This cross-module collaborative effect ensures the precise capture of user interests and the efficiency of product recommendations, preventing the repetition of recommending the same product to users. The experimental results were extensively tested on multiple real-world datasets. The results showed that the performance of MAHGRM was significantly superior to that of the comparison models such as GCN, GAT, HAN, HPN, OSGNN and ie-HGCN. On the MovieLens-1M dataset, the AUC, ACC and F1-score of MAHGRM reached 0.931, 0.867 and 0.863 respectively, achieving the best performance and fully demonstrating the superiority of MAHGRM in terms of performance.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 5","pages":"773-804"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11135463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896764","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}