Journal of Web Engineering

AI-Driven Next-Generation DDoS Mitigation in IoT: A Comprehensive Survey of Deep Learning and Large Language Models

2026-04-21T13:45:29+02:00

The global Internet of Things (IoT) network has experienced unprecedented expansion, reaching 75.44 billion connected devices worldwide. Due to this rapid growth, the IoT ecosystem has become larger but also more vulnerable to various threats in cyberspace. As a result, this situation has accelerated the development of more complex cyberattacks, especially Distributed Denial of Service (DDoS) attacks, which can damage systems by overwhelming network traffic. This attack exploits the inherent vulnerabilities of IoT networks with limited resources, leading to significant service interruptions, monetary losses, and potentially the failure of vital infrastructures. As a result, this survey presents a new taxonomy of attack types, a thorough assessment of conventional and AI-driven detection techniques, and a comparison of advanced countermeasures. This survey aims to provide a comprehensive and systematic overview of DDoS attack detection, prevention, and mitigation approaches for the IoT. A major contribution of this work is the inclusion of blockchain-based security frameworks, federated learning, deep learning, machine learning, and quantum computing paradigms in its discussion for reducing DDoS attacks on the IoT. Furthermore, real-world case studies highlight the severe impact of such attacks on mission-critical domains, including smart cities, healthcare, and industrial control systems (ICSs). This survey provides a basis for cybersecurity researchers, security experts, and hobbyists, grounded in the understanding gained from previous research.

Cross-Domain Fake News Classification via RoBERTa Fine-Tuning on Hybrid LIAR-FakeNewsNet Corpus with Deployable Web Inference

2026-05-05T00:47:49+02:00

The rapid spread of misinformation on social media platforms escalates a serious threat to world socio-political stability, health and financial security. While current automated fake news detection models boast impressive accuracy scores, these are often due to memorisation of the training domain rather than genuine generalisation to unseen examples. This study tackles the pressing need for domain-robust misinformation and disinformation detection through the creation of a highly adaptive and deployable classification system. The challenge stems from the severe drop in performance when exposed to the domain shift, especially with the emergence of generative language models that can perform style-based attacks. The proposed solution employs a RoBERTa-base transformer model, fine-tuned on a large, multi-domain corpus of 37,143 instances. This dataset mixes the short, politically focused statements of the LIAR dataset with the long, multi-topic articles from the FakeNewsNet dataset and ensures cross-domain generalisation. Using full-parameter fine-tuning and evaluation across different domains, the model attained 86.7% accuracy, 87.3% macro precision and 86.4% macro F1-score on a diverse test corpus, surpassing traditional baselines. Importantly, the assessment unveiled a conservative labelling tendency with a 90.4% precision in detecting fake news and a 93.1% recall in detecting genuine news which prevents the severe consequences of mistakenly accusing legitimate news. Moreover, the design breaks out of research-only models with its integration into a real-time web service (based on Flask), providing sub-second inferences with confidence scores. This approach simultaneously addresses the problem of cross-domain vulnerability of state-of-the-art natural language processing models and demands deployable interpretability, setting a new benchmark for the implementation of natural language processing models in the fight against advanced, online misinformation.

An Alignment-Driven Approach for Constructing SPARQL Federated Queries

2026-04-02T20:19:41+02:00

Integrating complementary information across distributed SPARQL endpoints remains a significant challenge. While query federation enables access across multiple endpoints, constructing federated queries requires both SPARQL expertise and knowledge of the underlying ontological schemas. Current query rewriting techniques primarily focus on vocabulary translation to retrieve equivalent data, often overlooking the integration of complementary information spread across sources. To address these limitations, we propose an alignment-driven approach that assists users in constructing federated queries by leveraging ontology alignments. Our approach joins class paths – user-selected sequences of ontology classes and relationships – from source and target ontologies, guided by alignment-based filtering, into a unified path that serves as the foundation for generating federated SPARQL queries capturing complementary aspects of the user’s intent. An experimental evaluation using OAEI Conference track datasets demonstrates that the generated queries consistently match reference queries in both structure and data retrieval across simple and complex mapping types, confirming the approach’s effectiveness in integrating complementary data from heterogeneous sources.

Security Architecture of WEB-based Education Management Platform Based on Zero-Trust Model: Protecting Sensitive Student Data and Teaching Resources

2026-04-07T20:56:29+02:00

In response to security risks such as data leakage and unauthorized access in educational Web platforms during digital transformation, this paper proposes a five-layer security architecture based on the zero-trust model. Following the principle of “never trust, always verify,” the architecture integrates identity management and access proxy technologies to build a collaborative protection system. It employs a multi-dimensional permission model for fine-grained access control, combines signature and behavior detection to strengthen Web attack protection, and leverages dynamic authorization evaluation, three-factor authentication, AES256 encryption, and distributed storage to ensure authentication and data security. Comparative experiments show that the architecture effectively mitigates unauthorized access, session hijacking, and abnormal authentication attacks, achieving a 97.0% defense success rate. Under 50 concurrent users, authentication delay remains within 4.2 ms, and the average response time in high-frequency access scenarios is 68 ms. Throughput meets the peak demands of education systems, with performance improved by 57.5% compared to traditional Web education platform architectures (TWEPA) and by 33.3% compared to general zero-trust architectures (ZTA). This solution provides a feasible approach for security enhancement and data compliance management in educational Web platforms.

A Web Engineering Approach to AI-Enabled Japanese Tutoring via Instructional Design Pattern Mining

2026-06-12T17:01:55+02:00

Despite the proliferation of artificial intelligence (AI)-enabled web applications for Japanese language learning, existing platforms suffer from three critical challenges: insufficient integration of AI capabilities with pedagogical design, limited scenario adaptability across proficiency levels, and suboptimal lightweight deployment on the browser side. To address these gaps and explore instructional design patterns tailored to diverse Japanese learning needs, this study focuses on web application technology implementation and interaction design as the core analytical dimensions. Three typical AI-enabled Japanese tutoring web applications were selected as research objects, and 192 Japanese learners participated in the empirical research. Quantitative data were obtained through technical disassembly, questionnaire surveys, and web-based behavior tracking; learning effects were verified by comparative experiments, and the weight of instructional design elements was determined via the analytic hierarchy process, finally extracting core instructional design modes suitable for the mainstream Japanese learning stages from beginner to upper-intermediate (Japanese-Language Proficiency Test [JLPT] N5–N2), with preliminary applicability discussion for the advanced stage (N1 and above). The results show that web-based lightweight AI technology deployment and real-time AI interactive feedback design are the core elements, with a combined weight of over 40%. There is a significant positive correlation between web-based AI technology implementation and user satisfaction, with the highest implementation effect and user acceptance in listening, speaking, and grammar modules. Compared with traditional Japanese web learning platforms, AI-enabled instructional design can improve learners’ listening and speaking abilities by 58.0%(M=58.0,SD=4.2,95% CI [56.4, 59.6], Cohen’s d=1.82, large effect), grammar analysis abilities by 46.5% (M=46.5,SD=5.1,95%CI [44.8, 48.2], Cohen’s d=1.45, large effect), and enhance learning sustainability by 85.7% (M=85.7,SD=6.3,95%CI[83.5,87.9], Cohen’s d=2.01, large effect), based on N5–N2 learner data with robust statistical power. Three core instructional design modes were identified, adapted to zero-based-N4 fragmented learning, N3–N2 systematic learning, and N1+ immersive learning, respectively. This study clarifies the core elements and optimization direction of such applications, providing standardized and practical theoretical references and paths for their design, development, and teaching practice.

Web Interactive Content Filtering Model Integrating Prompt Classifier and Intent Understanding

2026-06-09T02:55:49+02:00

To accurately identify and intercept harmful content generated in user interactions on Web platforms such as social media and online forums, this study proposes a Web interactive content filtering model that integrates Prompt classifiers and intent understanding. The model adopts a hierarchical review architecture with dual-module collaboration. The front-end deploys a Prompt classifier based on Deep Contrastive Learning (DCL), and uses the attention mechanism and deep convolutional network to perform rapid semantic analysis and preliminary screening of real-time comments and conversations. The back-end introduces an intent understanding module based on search-enhanced generation and Large Language Models (LLMs) to conduct in-depth contextual analysis of preliminary screening warning content to distinguish malicious attacks from legitimate sensitive topic discussions. Experiments showed that the system performed well on a test set containing 52,397 Chinese Web interactive texts. The Prompt classifier module achieved a precision of 95.2% and a recall of 96.5%. After collaborative calibration with the intent understanding module, the false alarm rate of the system was significantly reduced to 4.4%, while the recall rate remained at 96.1%. From the results, this study provides a content security solution for Web interactive scenarios that combines high accuracy, strong semantic understanding capabilities, and real-time response performance. It can effectively purify the cyberspace environment while ensuring freedom of interaction, with important practical application value.

Mitigating Volatility in P2E through Allowance Verification in Decentralized Platform

2026-05-22T01:25:04+02:00

Play-to-Earn (P2E) game economies integrated with Automated Market Maker (AMM) protocols face persistent sustainability challenges driven by whale-induced price volatility and the absence of effective token-burning mechanisms. These issues erode economic fairness and discourage long-term participation within web-based blockchain gaming platforms. This paper proposes EG-AMM (Epoch-Guarded Automated Market Maker), a software engineering framework that adds a governance-aware volatility protection layer to existing AMM-based P2E infrastructures without modifying the underlying pricing model. Inspired by circuit breaker mechanisms in traditional finance, EG-AMM enforces per-epoch dynamic swap allowances for individual players, computed from price impact tolerance and configurable through Decentralized Autonomous Organization (DAO) parameters. The framework employs Merkle-proof-based cryptographic verification and on-chain accounting to ensure allowance limits are transparent and tamper-resistant. Unlike existing pool-level approaches such as static transaction caps or dynamic fee schedules, EG-AMM operates at the individual player level, curbing whale dominance while preserving equitable market access for ordinary players and newcomers. Preliminary analysis and game-theoretic simulation confirm improvements in price stability, participation fairness, and economic resilience, offering a practical and deployable path toward sustainable token ecosystems in blockchain-integrated gaming applications.

Multimodal Evidence-Based License Compliance Verification for KOGL Open Government Works on the Web

2026-07-31T03:47:33+02:00

The reuse of open government works on the web has increased rapidly with the expansion of public data policies and artificial intelligence (AI)-assisted content utilization. In Korea, Korea Open Government Licenses (KOGLs) permit the reuse of public works under conditions such as attribution, non-commercial use, and no-derivatives. However, these conditions are frequently violated or misunderstood in web pages, advertisements, social media posts, and reused digital content. Since compliance-related evidence is distributed across textual descriptions, captions, license metadata, embedded image text, visual changes, and page-level context, manual inspection, and single-modal analysis are not sufficient for scalable web-based compliance review.

This paper proposes a multimodal evidence-based method for KOGL license compliance risk prediction in web environments. The proposed method combines textual context, KOGL metadata, optical character recognition (OCR) extracted image text, and visual features to classify four risk types: attribution risk, non-commercial use risk, no-derivatives risk, and false KOGL claim risk. A lightweight sequential context model captures the contextual flow of compliance-related cues, while the decision module produces both risk probabilities and evidence-based explanations. Experiments on a KOGL-oriented dataset show that the proposed method achieves higher accuracy, macro-F1, and area under the curve (AUC) than text-only, image-only, and simple fusion baselines. The results demonstrate that multimodal web evidence improves compliance risk prediction and provides practical support for human review in public content management, open government data curation, and AI training data inspection.