Frontiers in Big Data最新文献

{"title":"Algorithmic advances in smart TV content recommendation: a structured evidence-mapping review.","authors":"Zhe Chen, Jing He, Yuanjia Gong, Junge Liang, Jilong Li","doi":"10.3389/fdata.2026.1927033","DOIUrl":"10.3389/fdata.2026.1927033","url":null,"abstract":"<p><p>Smart TV recommenders operate under constraints that are less prominent on personal devices: viewing is often passive, one account may represent several viewers, direct feedback is scarce, and programmes are long and semantically rich. We mapped research published from 2015 to 2025 using a structured evidence-review protocol. The submitted bibliography comprised 122 DOI-bearing references, including two foundational sources and 120 records used in the evidence map. Crossref verified 116 records; six stable arXiv DOI records were retained. Searches across six sources yielded 2,526 database-level results. A combined deduplication and title/abstract screening stage removed 2,414 records, leaving 112 unique records for full-text assessment; all 112 full texts were retrieved, 87 records were excluded, and 25 met the eligibility criteria. These 25 studies were combined with the 95-study initial corpus to form the 120-study evidence map. Separately, we conducted an independent dual-reviewer eligibility audit of 126 records, comprising all 120 included studies and six representative boundary exclusions. Agreement was 97.6% (123/126; Cohen's κ = 0.788). Six records were excluded by both reviewers, and three discordant judgments were resolved by joint full-record review. For analysis, each study was assigned one primary technical category and one of six mutually exclusive primary functional goals; secondary technical labels captured hybrid methods. Evidence reporting was profiled separately for data transparency, reproducibility, evaluation design, external validation, and deployment validation. The final literature corpus encompasses intelligent recommendation systems based on deep learning, sequence analysis, graph theory, shared accounts, and multimodal approaches.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1927033"},"PeriodicalIF":3.8,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven adaptive hybrid models for exchange rate return forecasting.","authors":"Olumide Sunday Adesina, Lawrence Ogechukwu Obokoh","doi":"10.3389/fdata.2026.1917723","DOIUrl":"10.3389/fdata.2026.1917723","url":null,"abstract":"<p><strong>Background: </strong>Exchange-rate return forecasting is challenging because financial time series may exhibit linearity, nonlinearity, regime-switching behavior, and volatility. To address these complexities, two adaptive hybrid forecasting frameworks were developed: ATW-HyF A, which dynamically combines ARIMA, SETAR, and ANN forecasts using inverse-variance weighting, and ATW-HyF B, which extends the framework by incorporating a GARCH(1,1) volatility layer to model the conditional variance of forecast errors.</p><p><strong>Methods: </strong>The robustness of the proposed frameworks was assessed using simulated data and three foreign exchange return series: USD/NGN, EUR/USD, and GBP/USD. Forecast performance was evaluated within a rolling-origin forecasting framework and compared with competing forecasting models.</p><p><strong>Results: </strong>The simulation results showed that the ATW-HyF models achieved the lowest forecast errors among the competing models. However, their performance on the empirical exchange-rate series was mixed, with other models achieving lower forecast errors for some currency pairs. In particular, the ARIMA-ANN hybrid performed best for USD/NGN and EUR/USD, whereas ATW-HyF B performed best for GBP/USD.</p><p><strong>Discussion: </strong>The findings indicate that forecasting performance depends on the characteristics of the underlying exchange-rate series rather than on model complexity alone. Combining models with complementary predictive strengths can improve forecasting performance, particularly when the data exhibit linear, nonlinear, and volatility-related patterns. The incorporation of adaptive weighting and volatility modeling therefore provides a flexible approach to exchange-rate return forecasting under evolving market conditions.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1917723"},"PeriodicalIF":3.8,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13612353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IOTTRUST: graph-based anomaly detection for IoT intrusion using network flow topology and community structure analysis on UNSW-NB15.","authors":"Nachaat Mohamed, Hamed Taherdoost","doi":"10.3389/fdata.2026.1885965","DOIUrl":"10.3389/fdata.2026.1885965","url":null,"abstract":"<p><strong>Introduction: </strong>Conventional machine learning approaches to IoT intrusion detection treat each network flow record as an independent observation, discarding the relational structure that connects flows across source IPs, destination IPs, and subnet communities. This article presents IOTTRUST, a graph-augmented intrusion detection framework for IoT networks that constructs a directed network flow graph from the UNSW-NB15 dataset and enriches per-flow machine learning with 24 graph-derived topology features computed per source and destination IP.</p><p><strong>Methods: </strong>All graph topology statistics are computed exclusively from the training partition and propagated to the test partition without access to test-set labels, eliminating the temporal leakage present in an earlier version of this study. The experimental design isolates the contribution of graph topology through a three-track comparison conducted on an identical 40,000-flow stratified sample with a fixed 70/30 split (random seed 42): a Flow-Only baseline using the standard 43 UNSW-NB15 features, a Graph-Only model trained exclusively on 24 graph topology features, and the full IOTTRUST Hybrid model combining 22 numeric flow features with 24 graph features (46 total).</p><p><strong>Results: </strong>Under this leakage-free, size-matched evaluation, Flow-Only achieves 98.98% accuracy and AUC = 0.9995 with FPR = 0.81%; Graph-Only alone reaches 97.89% accuracy and AUC = 0.9974 with FPR = 1.79%; and IOTTRUST Hybrid achieves 99.07% accuracy, AUC = 0.9996, Precision = 97.09%, Recall = 98.30%, and FPR = 0.74%, a modest but statistically significant improvement over Flow-Only (McNemar <i>p</i> = 0.295 on the held-out test set, paired <i>t</i>-test on five-fold cross-validated <i>F</i>1 <i>p</i> = 0.026) and a substantial improvement over Graph-Only alone (McNemar <i>p</i> < 0.001).</p><p><strong>Discussion: </strong>Community structure analysis on the training graph confirms that the attacker subnet (175.45.176.x, four IPs) accounts for over 90% of attack flows, exhibiting distinctive graph signatures that graph features capture directly. Feature importance analysis shows that 12 of the top 15 most important features in the leakage-free Hybrid model remain graph topology features, accounting for 60.4% of the top-15 importance mass, confirming that network relational structure continues to carry predictive information even under this stricter evaluation protocol.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1885965"},"PeriodicalIF":3.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-tuning multilingual sentence transformers for low-resource skill-concept matching across Kazakh, Russian, and English.","authors":"Sandugash Serikbayeva, Madina Sambetbayeva, Valiya Ramazanova, Aigerim Yerimbetova, Zhanar Lamasheva, Zhanna Sadirmekova, Ardak Batyrkhanov, Yersaiyn Mailybayev","doi":"10.3389/fdata.2026.1900836","DOIUrl":"10.3389/fdata.2026.1900836","url":null,"abstract":"<p><strong>Introduction: </strong>Matching short, specialized skill expressions across English, Russian, and Kazakh is challenging because general-domain multilingual encoders underperform on terse, code-mixed, domain-specific phrases, particularly in the low-resource Kazakh setting.</p><p><strong>Methods: </strong>We fine-tuned a multilingual Sentence Transformer using a staged Multiple Negatives Ranking objective on a trilingual paraphrase corpus, including Russian augmentation pairs. We evaluated skill matching and semantic similarity across languages and assessed the resulting embeddings through downstream skill-taxonomy clustering.</p><p><strong>Results: </strong>Fine-tuning preserved English performance while improving Russian and Kazakh similarity quality. Russian cosine Pearson correlation increased from 0.8125 to 0.8221, while Kazakh cosine Pearson increased from 0.5989 to 0.6050 and Kazakh dot-product Pearson from 0.4487 to 0.4912. Agglomerative clustering improved mean silhouette from 0.27126 to 0.2851 and reduced erroneous clusters from 19.74% to 13.97%.</p><p><strong>Discussion: </strong>The results provide evidence consistent with cross-lingual transfer as an important mechanism of improvement for Kazakh. They also motivate language-specific threshold calibration and demonstrate that intrinsic similarity improvements translate into a cleaner downstream skill taxonomy.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1900836"},"PeriodicalIF":3.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Smart forecasting: deep learning and explainable AI for real-world time series prediction.","authors":"Leonard Barolli, Antonino Ferraro, Antonio Galli, Francesco Moscato","doi":"10.3389/fdata.2026.1954626","DOIUrl":"10.3389/fdata.2026.1954626","url":null,"abstract":"","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1954626"},"PeriodicalIF":3.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of DistilBERT and Perceiver for deceptive review detection in hospitality review data.","authors":"Ioana Ximena Remeş, Felicia Mirabela Costea, Cornelia Aurora Gyorödi","doi":"10.3389/fdata.2026.1918383","DOIUrl":"https://doi.org/10.3389/fdata.2026.1918383","url":null,"abstract":"<p><p>Deceptive reviews on hospitality platforms can undermine consumer trust and affect the reliability of online reputation systems. This study presents a comparative empirical evaluation of two deep learning pipelines for deceptive review detection: a DistilBERT-based pipeline fine-tuned end-to-end on review text and complemented with rating and sentiment features, and a Perceiver-based pipeline operating on fixed sentence embeddings generated by a frozen encoder. The experiments were conducted on a combined English-language corpus of 3,322 hotel reviews assembled from the Myleott benchmark corpus and a Kaggle dataset of authentic Ritz-Carlton New York reviews, comprising approximately 2,520 genuine and 800 deceptive reviews. The corpus was evaluated using stratified 80/20 train-test splits, with 2,657 reviews used for training and 665 for testing in each split, and the deep learning models were trained for 10 epochs. To assess the robustness of the findings, the evaluation was repeated across three independent random seeds and compared with majority-class and TF-IDF plus logistic regression baselines. DistilBERT achieved the most stable performance, reaching an average accuracy of 0.9348 ± 0.0014, an F1-score of 0.9569 ± 0.0013, and a Matthews correlation coefficient of 0.8240 ± 0.0012. In contrast, the original Perceiver pipeline showed a strong tendency to collapse toward the majority class, which limited its ability to identify deceptive reviews despite apparently high recall. Although class-weighted training improved Perceiver's behavior, its results remained less reliable and more variable than those obtained with DistilBERT. The balanced TF-IDF plus logistic regression baseline performed close to DistilBERT, indicating that well-configured traditional natural language processing methods remain competitive on datasets of this size. The ablation analysis further indicates that the rating and sentiment features contribute primarily to the stability of the model across different train-test splits, rather than providing a substantial independent gain in discriminative performance. The results indicate that the fine-tuned DistilBERT pipeline provides the most robust option in the evaluated setting, while also emphasizing the importance of reporting corpus composition, test-set size, class imbalance, and imbalance-aware metrics when developing deceptive review detection systems for hospitality review data.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1918383"},"PeriodicalIF":3.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149032207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction-aware urban crime analytics from incomplete judicial records using heterogeneous graph temporal imputation.","authors":"Wen Xu, Yong Dai, Qi Zhang, Keyu Chen, Sanxia Zeng","doi":"10.3389/fdata.2026.1876811","DOIUrl":"10.3389/fdata.2026.1876811","url":null,"abstract":"<p><strong>Introduction: </strong>Incomplete judicial records constrain reliable crime analytics because missing attributes, heterogeneous case descriptions, mixed variable types, and imbalanced charge distributions reduce their analytical value. This study developed a reconstruction-aware framework for drug-crime charge classification from incomplete judicial records.</p><p><strong>Methods: </strong>Drug-related judicial documents from Chengdu, China, covering 2014-2021 were screened, cleaned, and converted into structured person-level observations. The final dataset comprised 12,620 valid documents and 15,184 observations with 16 structured features. A Heterogeneous Graph Convolution Temporal Autoencoder (HetGConv-TAE) was developed to jointly model heterogeneous case-attribute relations and temporal changes in case composition. Performance was evaluated under 10%, 20%, and 30% controlled missingness, followed by XGBoost charge classification and SHAP interpretation.</p><p><strong>Results: </strong>HetGConv-TAE achieved the best mean reconstruction performance across conventional, static graph, temporal, and relational graph baselines. XGBoost trained on HetGConv-TAE reconstructed data achieved weighted F1 scores close to 80% and ROC-AUC values above 90%. SHAP analysis identified drug weight, place category, and administrative district as the most influential predictors across charge categories.</p><p><strong>Discussion: </strong>Combining label-excluded heterogeneous graph reconstruction, temporal encoding, downstream classification, and interpretable analysis improves the analytical usefulness of incomplete judicial archives. The framework is intended to support data-quality assessment and aggregate public-safety research rather than automated legal decision-making.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1876811"},"PeriodicalIF":3.8,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13590562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven cybersecurity for industrial internet of things: architectures, challenges, datasets, and future research directions.","authors":"Siddhartha Singhal, Kakelli Anil Kumar","doi":"10.3389/fdata.2026.1938279","DOIUrl":"10.3389/fdata.2026.1938279","url":null,"abstract":"<p><p>While the Industrial Internet of Things (IIoT) has a wide range of applications in the modern era, including smart manufacturing, healthcare, transportation, energy, and critical infrastructure, the multitude of devices and distributed communication, alongside the convergence of cyber and physical systems, makes these environments vulnerable to more advanced cyber-attacks. Traditional signature or pattern-based security solutions continue to be ineffective against new, sneaky, and zero-day attack strategies, fueling the interest in AI-powered cybersecurity. This review aims to analyze the latest developments systematically in intelligent threat detection and defense in IIoT environments. The review critically analyzes the cybersecurity research published over the past few years (2020-2026) on cyber threats across the various layers of the IIoT architecture, publicly available cybersecurity datasets, evaluation practices, and AI-based intrusion detection methods, such as machine learning, deep learning, hybrid architectures, transformers, graph neural networks, federated learning, reinforcement learning, and explainable AI. High benchmark performance alone is not sufficient to claim cybersecurity effectiveness, as the synthesis shows persistent limitations in cross-domain generalization, computational overhead, explainability, adversarial robustness, edge deployment, and operational validation. Emerging research priorities included in this review are lightweight edge intelligence, continual and adaptive learning, explainable federated intelligence, digital-twin-enabled security, foundation-model-driven cyber intelligence, autonomous cyber defense, and trustworthy AI. This review provides a pathway toward resilient, adaptive, and operationally deployable cybersecurity solutions for next-generation IIoT and highlights the PRISMA-based identification process.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1938279"},"PeriodicalIF":3.8,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13592189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HK-DeepIV: heat-kernel geometric diagnostics and early-warning signals for curvature-induced interference in financial correlation networks.","authors":"Ntebogang Dinah Moroke","doi":"10.3389/fdata.2026.1925931","DOIUrl":"10.3389/fdata.2026.1925931","url":null,"abstract":"<p><p>Financial correlation networks under infrastructure shocks undergo curvature-driven interference that standard estimators cannot detect. This study introduces Heat-Kernel Deep Instrumental Variables (HK-DeepIV), a geometric diagnostic and early-warning framework modeling shock propagation as heat diffusion on the Riemannian manifold of Johannesburg Stock Exchange (JSE) asset-return correlations. Three formal results underpin the architecture: a non-parametric identification result for the projection of the structural function onto the leading heat-kernel eigenfunction (<i>k</i> = 1; a scalar instrument can identify at most one linear combination of the eigenfunctions, and no claim is made beyond that projection); double robustness via Neyman orthogonality; and Corollary 3.2, showing that unit-level distinguishability collapses exponentially in diffusion time whenever Ollivier-Ricci curvature is positive; a purely geometric statement providing the basis for the fragility diagnostics developed here. Applied to 60 JSE tickers (2,832 trading days, 2015-2025) with Eskom load-shedding as the treatment (2022-2025), the trained model produces an ATE of +383 bp, reported as an overfitting artifact. Five independent baselines converge on smaller, mostly negative estimates. The most credibly conditioned conditional-association estimate is double machine learning [-20.75 bp, heteroskedasticity-and-autocorrelation-consistent (HAC)-corrected 95% CI [-51.62, 10.12] bp, <i>p</i> = 0.188], which is not significant at conventional levels; consistent with the instrument exogeneity caveat (5-day lagged return balance test, <i>p</i> = 0.007) and the descriptive framing of all estimates. The instrument [48-h-ahead Eskom stage forecast, Corr(<i>Z</i> _<i>t</i> , <i>D</i> _<i>t</i> )≈0.71, first-stage <i>F</i> = 47.3] is distinct from the treatment (realized Stage ≥2 binary), but exogeneity is not confirmed; all estimates are conditional associations. The Fiedler eigenvalue (mean 0.3827, minimum 0.1819) and mean Ollivier-Ricci curvature (0.5517, persistently positive) provide computable real-time fragility indicators. Across the four most severe load-shedding quarters, Fiedler and Ricci diagnostics offer comparable early-warning signals (mean lead-time difference -0.5 trading days); neither is systematically superior, but their combination is more informative than either alone. The framework contributes toward real-time spectral monitoring of infrastructure-driven systemic risk, supporting Uited Nations Sustainable Development Goal (UN SDG 9) in emerging markets.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1925931"},"PeriodicalIF":3.8,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13581490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HyperIDS: a hypergraph learning and quantum-inspired transformer ensemble framework for IoT intrusion detection.","authors":"Samayank Goel, Logeswari Govindaraj, Tamilarasi Kathirvel Murugan","doi":"10.3389/fdata.2026.1901121","DOIUrl":"10.3389/fdata.2026.1901121","url":null,"abstract":"<p><p>The unprecedented increase in the number of Internet of Things (IoT) devices has widened the attack surface of modern-day networks, making them vulnerable to various cyberattacks. Conventional intrusion detection mechanisms face challenges in identifying the subtle correlations between network traffic characteristics and providing consistent detection rates in varying heterogeneous environments. With this context, this research aims at introducing HyperIDS, an innovative Intrusion Detection System (IDS) which combines Hypergraph Learning, Quantum-Inspired Feature Selection and Optimization, and Transformer-based Ensemble Classifier for intelligent IoT cyberattack detection. First, Dual-Fitness Enhanced Gaussian Quantum Particle Swarm Optimization (DFE-GQPSO) approach is utilized to select the most relevant traffic characteristics while reducing feature space and computational cost. These selected traffic features are then converted to a hypergraph form, allowing higher order relations between different entities of the network to be captured. Following this step, Hypergraph Neural Networks (HGNN) is deployed to generate structural and relational representations from the hypergraph structure of the dataset. Long-term dependencies and attack patterns are subsequently extracted using a transformer encoder. The final classification process involves combining CatBoost and XGBoost using stacking ensemble method. In addition, a SHAP-based explainability module is incorporated to ensure transparency and trustworthiness of the developed system. In order to evaluate the proposed framework, HyperIDS is experimentally tested against two commonly used cyber security datasets, namely, TON_IoT and Bot-IoT. Experimental findings have shown superior effectiveness of HyperIDS in detecting cyber-attacks with 98.92, 98.81, 98.76, and 98.78% of accuracy, precision, recall, and F1 score, respectively on the TON_IoT dataset. Similarly, accuracy, precision, recall, and F1 scores of HyperIDS reach 99.14, 99.05, 99.01, and 99.03% on the Bot-IoT dataset. Comparison with conventional machine learning (ML), deep learning (DL), and hybrid intrusion detection techniques have proven HyperIDS's superiority in detecting cyberattacks on IoT infrastructure.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1901121"},"PeriodicalIF":3.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13574614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}