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        <title>Frontiers in Big Data | Cybersecurity and Privacy section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/big-data/sections/cybersecurity-and-privacy</link>
        <description>RSS Feed for Cybersecurity and Privacy section in the Frontiers in Big Data journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-13T15:27:04.775+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1733733</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1733733</link>
        <title><![CDATA[Strategic cyber intelligence with advanced analytics in Latin America: a perspective]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Tamara Briones-Lascano</author><author>Vanessa Vergara-Lozano</author><author>Alex Miranda-Andrade</author>
        <description><![CDATA[Digital transformation in Latin America has widened the attack surface, while exposing long-standing gaps in policy, capability, and data stewardship. From this perspective, we argue that the region can move from reactive cybersecurity to strategic cyber intelligence by embedding advanced analytics into an intelligence cycle that connects multisource data, governed models, and operational playbooks with clear accountability. We synthesize the demonstrated technical gains, diagnose implementation constraints, and outline a near-term agenda that includes a regional maturity index, comparative outcome studies, and decision research on explainability and bias. Thus, our position was practical. Analytics amplifies a good strategy only when governance, trustworthy data, and skilled teams are in place. This Perspective contributes a strategic analytical framework that links advanced analytics, governance, and decision-making to strengthen cyber intelligence and digital resilience in Latin America.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1821270</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1821270</link>
        <title><![CDATA[Novel approach of encrypted network traffic classification using deep convolutional neural network with Artificial Bee Colony and Genetic Algorithm]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sujan Kumar Mohanty</author><author>Satyajit Rath</author><author>Satya Ranjan Sahu</author><author>Bikram Kumar Parida</author><author>Rakesh Chandra Balabantaray</author>
        <description><![CDATA[The encode network traffic makes it difficult to perform successful and dynamic classification. This paper will present the use of a hybrid model to be used with the publicly available QUIC dataset to classify VPN and non-VPN encrypted traffic based on a Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) network, which is optimized by the Artificial Bee Colony (ABC) and Genetic Algorithm (GA). The method involves multi-angle processing - preprocessing, Min-Max normalization, and features selection of with correlation analysis, Fisher Score, and mutual information to obtain a tiny, but meaningful feature set (Size, Batch Cache, Delta Previous Packet). The chosen features are translated to 2D tensors through a sliding time window of consecutive packets, which allows the spatio-temporal DCNN+LSTM architecture to represent the level of intra- and inter-packet feature associations as well as inter- and intra-packet time dynamics. The disadvantages of single-optimization are overcome using a dual metaheuristic optimization strategy again whereby the work of the global hyperparameter exploration is done using ABC and the structural optimization is done using GA. The imbalance of classes is reduced with weighted loss functions and stratified data division. The accuracy of the model is 99.66% with 0.994 ROC-AUC and 0.987 PR-AUC and its MCC is 0.963 which is even greater than that of the traditional classifiers (Decision Tree, Random Forest, SVM, KNN), individual deep-learning models (CNN, LSTM), and image-based FlowPic method. Three-quarters of stratified cross-validation marks the case of consistent generalization (99.53% ± 0.09% mean accuracy), and an ablation study confirms the value of any one of the components. The findings prove that the presented framework can be applied to monitor the network traffic on encrypted networks which are security-sensitive and in real-time.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1768366</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1768366</link>
        <title><![CDATA[Beyond performance metrics: evaluating the unique value of generative AI in hybrid cybersecurity threat detection]]></title>
        <pubdate>2026-04-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Juan Antonio González-Ramos</author><author>Pablo Chamoso</author>
        <description><![CDATA[IntroductionThis study examines the role of generative artificial intelligence (GenAI) in cybersecurity threat detection, focusing on its usefulness in workflows that support human decision-making.MethodsExperiments were performed on the BODMAS dataset (134,435 samples) and a smaller exploratory subset of UNSW-NB15. State-of-the-art machine learning (ML) classifiers were compared with a zero-shot large language model (LLM) using standard classification metrics, while also considering latency, cost, and hallucination risk.ResultsML classifiers consistently outperformed the LLM-based system on standard detection metrics. However, the LLM showed value in cases of ambiguity, where it could provide short plain-language explanations, organize alert-related context, and generate initial interpretations for instances that did not match learned classes.DiscussionGenAI is unlikely to replace ML-based detection methods, but it can provide useful interpretive support for ambiguous or unfamiliar alerts. A hybrid pipeline is therefore proposed, in which ML handles high-confidence and time-sensitive decisions, while the LLM is used selectively for low-confidence cases or when explanatory support is needed. Human oversight remains necessary to address hallucination risk and ensure reliability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1770989</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1770989</link>
        <title><![CDATA[Spatiotemporal deep learning framework for predictive behavioral threat detection in surveillance footage]]></title>
        <pubdate>2026-02-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Asha Aruna Sheela Matta</author><author>Venkata Purna Chandra Sekhara Rao Manukonda</author>
        <description><![CDATA[Anomaly detection in video surveillance remains a challenging problem due to complex human behaviors, temporal variability, and limited annotated data. This study proposes an optimized spatiotemporal deep learning (DL) framework that integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) network for temporal dependency modeling. The CNN processes frame-level appearance information, while the LSTM captures sequential motion patterns across video frames, enabling effective representation of anomalous activities. Hyperparameter optimization and regularization strategies are employed to improve convergence stability and generalization performance. The proposed model is evaluated on the DCSASS surveillance dataset and the experimental results demonstrate that the optimized CNN-LSTM framework achieves an accuracy of 98.1%, with consistently high precision, recall, and F1-score across 3-fold, 5-fold, and 10-fold cross-validation settings. Comparative analysis shows that the proposed method outperforms conventional machine learning models and recent deep learning baselines, highlighting its effectiveness and robustness for practical video-based anomaly detection in surveillance environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1681382</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1681382</link>
        <title><![CDATA[Federated learning for teacher data privacy protection: a study in the context of the PIPL]]></title>
        <pubdate>2026-02-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shanwei Chen</author><author>Xiu Zhi Qi</author><author>Xue Hui Han</author><author>Zhao Chen Fan</author><author>Le Le Wang</author>
        <description><![CDATA[BackgroundThe Personal Information Protection Law (PIPL) in China imposes strict requirements on personal data handling, particularly in educational contexts where teacher data privacy is critical. Traditional centralized machine learning approaches pose significant risks of data breaches and non-compliance. Federated Learning (FL) offers a promising decentralized alternative by enabling collaborative model training without sharing raw data.MethodsThis study combines quantitative simulations and qualitative compliance analysis to evaluate FL frameworks under PIPL principles, with a focus on Differential Privacy as the primary empirically validated mechanism for noise addition and privacy guarantee. Other techniques, such as Secure Multi-Party Computation (SMC), are analyzed theoretically for their alignment with PIPL requirements like data minimization, anonymization, and encrypted transmission.ResultsExperimental simulations demonstrate that FL effectively reduces data breach risks compared to centralized methods. It achieves principle-level compliance with PIPL through local data processing, differential privacy mechanisms, and secure aggregation, leading to improved privacy preservation while maintaining model performance.ConclusionFL conceptually supports teacher data privacy protection under the PIPL framework. This study proposes a tailored compliance framework that integrates FL with privacy-enhancing technologies, offering theoretical foundations and practical recommendations for educational institutions and technology implementers to deploy privacy-preserving machine learning solutions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1659026</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1659026</link>
        <title><![CDATA[EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare]]></title>
        <pubdate>2026-01-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bela Shrimali</author><author>Jenil Gajjar</author><author>Swapnoneel Roy</author><author>Sanjay Patel</author><author>Kanu Patel</author><author>Ramesh Ram Naik</author>
        <description><![CDATA[Recent advances in Artificial Intelligence have highlighted the role of Machine Learning in healthcare decision-making, but centralized data collection raises significant privacy risks. Federated Learning addresses this by enabling collaborative training across multiple clients without sharing raw data. However, Federated Learning remains vulnerable to security threats that can compromise model reliability. This paper proposes a dual-security Federated Learning framework that integrates Fernet Symmetric Encryption for secure transmission of model updates using symmetric encryption and an Intrusion Detection System to detect anomalous client behavior. Experiments on a publicly available healthcare dataset show that the proposed system enhances privacy and robustness compared to traditional FL. Among tested models, including Logistic Regression, Random Forest, and SVC, the ensemble method achieved the best performance with 99% accuracy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1688091</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1688091</link>
        <title><![CDATA[Transparent and trustworthy CyberSecurity: an XAI-integrated big data framework for phishing attack detection]]></title>
        <pubdate>2025-12-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhammad Nauman</author><author>Hafiz Muhammad Usman Akhtar</author><author>Huseyn Gorbani</author><author>Muhammad Hadi Ul Hassan</author><author>Muhammad A. B. Fayyaz</author>
        <description><![CDATA[IntroductionThe exponential growth of heterogeneous, high-velocity CyberSecurity data generated by modern digital infrastructures presents both opportunities and challenges for threat detection, especially against increasingly sophisticated cyber-attacks. Traditional security tools struggle to process such data effectively, highlighting the need for scalable Big Data Analytics and advanced Machine Learning (ML) techniques. However, the black-box nature of many ML models limits interpretability, trust, and regulatory compliance in high-stakes environments.MethodsThis study proposes an integrated framework that combines Big Data technologies, ML models, and Explainable Artificial Intelligence (XAI) to enable accurate, transparent, and real-time phishing attack detection. The framework leverages distributed computing and stream processing for efficient handling of large and diverse datasets while incorporating XAI methods to generate human-understandable model explanations.ResultsExperimental evaluation conducted on four publicly available CyberSecurity datasets demonstrates improved phishing detection performance, enhanced interpretability of model decisions, and actionable insights into malicious URL behavior and patterns.DiscussionThe proposed approach advances interpretable and scalable CyberSecurity analytics by addressing the gap between predictive accuracy and decision transparency. By integrating Big Data processing with XAI-driven ML, the framework offers a trustworthy solution for real-time threat detection, supporting informed decision-making and regulatory compliance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1683027</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1683027</link>
        <title><![CDATA[Privacy protection method for ADS-B air traffic control data based on convolutional neural network and symmetric encryption]]></title>
        <pubdate>2025-12-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Changsheng Ma</author><author>Ruchun Jia</author><author>Jing Lou</author><author>Mingqian Wang</author>
        <description><![CDATA[IntroductionADS-B (Automatic Dependent Surveillance-Broadcast) is a key surveillance technology in modern air traffic management, which broadcasts real-time aircraft information such as position, speed, and altitude for enhanced flight tracking and safety. However, the open broadcast nature of ADS-B communication raises significant privacy concerns, as sensitive data can be easily intercepted and misused. Research on privacy protection for ADS-B air traffic control data faces significant challenges, making the effective mining and safeguarding of privacy information a critical research focus.MethodsThis study proposes a novel privacy protection method that integrates deep learning with symmetric encryption. Specifically, by analyzing the ADS-B air traffic monitoring architecture, we mine and normalize privacy-related data to develop a Convolutional Neural Network (CNN)-based classification model for accurate identification of sensitive information.ResultsExperimental results demonstrate that the proposed method effectively scrambles the original privacy information, with no instances of data theft or malicious damage. For data volumes of 10GB, 20GB, 30GB, and 40GB, the encryption times are 20.36ms, 30.56ms, 40.35ms, and 50.36ms, respectively, showcasing its efficiency.DiscussionCompared to existing methods, our approach achieves shorter encryption times while maintaining robust privacy protection. Future work could explore integrating advanced encryption technologies with state-of-the-art deep learning algorithms to further enhance the security of privacy protection in ADS-B systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1720525</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1720525</link>
        <title><![CDATA[Detecting anti-forensic deepfakes with identity-aware multi-branch networks]]></title>
        <pubdate>2025-12-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mingyu Zhu</author><author>Jun Long</author>
        <description><![CDATA[Deepfake detection systems have achieved impressive accuracy on conventional forged images; however, they remain vulnerable to anti-forensic or adversarial samples deliberately crafted to evade detection. Such samples introduce imperceptible perturbations that conceal forgery artifacts, causing traditional binary classifiers—trained solely on real and forged data—to misclassify them as authentic. In this paper, we address this challenge by proposing a multi-channel feature extraction framework combined with a three-class classification strategy. Specifically, one channel focuses on extracting identity-preserving facial representations to capture inconsistencies in personal identity traits, while additional channels extract complementary spatial and frequency domain features to detect subtle forgery traces. These multi-channel features are fused and fed into a three-class detector capable of distinguishing real, forged, and anti-forensic samples. Experimental results on datasets incorporating adversarial deepfakes demonstrate that our method substantially improves robustness against anti-forensic attacks while maintaining high accuracy on conventional deepfake detection tasks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1641714</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1641714</link>
        <title><![CDATA[Robust contactless fingerprint authentication using dolphin optimization and SVM hybridization]]></title>
        <pubdate>2025-12-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jenisha Rachel</author><author>Ezhilmaran Devarasan</author>
        <description><![CDATA[The field of contactless fingerprint (CLFP) recognition is rapidly evolving, driven by its potential to offer enhanced hygiene and user convenience over traditional touch-based systems without compromising security. This study introduces a contactless fingerprint recognition system using the Dolphin Optimization Algorithm (DOA), a nature-inspired technique suited for complex optimization tasks. The Histogram of Oriented Gradients (HOG) method is applied to reduce image features, with DOA optimizing the feature selection process. To boost prediction accuracy, we fused the DOA with a Support Vector Machine (SVM) classifier, creating a hybrid (DOA-SVM) that leverages the global search prowess of DOA alongside the reliable classification strength of SVM. Additionally, two more hybrid models are proposed: one combining Fuzzy C-Means (FCM) with DOA-SVM, and another combining Neutrosophic C-Means (NCM) with DOA-SVM. Experimental validation on 504 contactless fingerprint images from the Hong Kong Polytechnic University dataset demonstrates a clear performance progression: DOA (91.00%), DOA-SVM (94.07%), FCM-DOA-SVM (96.03%), and NCM-DOA-SVM (98.00%). The NCM-DOA-SVM approach achieves superior accuracy through effective uncertainty handling via neutrosophic logic while maintaining competitive processing efficiency. Comparative analysis with other bio-inspired methods shows our approach achieves higher accuracy with reduced computational requirements. These results highlight the effectiveness of combining bio-inspired optimization with traditional classifiers and advanced clustering for biometric recognition.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1617978</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1617978</link>
        <title><![CDATA[Robust detection framework for adversarial threats in Autonomous Vehicle Platooning]]></title>
        <pubdate>2025-11-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Stephanie Ness</author>
        <description><![CDATA[IntroductionThe study addresses adversarial threats in Autonomous Vehicle Platooning (AVP) using machine learning.MethodsA novel method integrating active learning with RF, GB, XGB, KNN, LR, and AdaBoost classifiers was developed.ResultsRandom Forest with active learning yielded the highest accuracy of 83.91%.DiscussionThe proposed framework significantly reduces labeling efforts and improves threat detection, enhancing AVP system security.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1669488</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1669488</link>
        <title><![CDATA[CrossDF: improving cross-domain deepfake detection with deep information decomposition]]></title>
        <pubdate>2025-11-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shanmin Yang</author><author>Hui Guo</author><author>Shu Hu</author><author>Bin Zhu</author><author>Ying Fu</author><author>Siwei Lyu</author><author>Xi Wu</author><author>Xin Wang</author>
        <description><![CDATA[Deepfake technology represents a serious risk to safety and public confidence. While current detection approaches perform well in identifying manipulations within datasets that utilize identical deepfake methods for both training and validation, they experience notable declines in accuracy when applied to cross-dataset situations, where unfamiliar deepfake techniques are encountered during testing. To tackle this issue, we propose a Deep Information Decomposition (DID) framework to improve Cross-dataset Deepfake Detection (CrossDF). Distinct from most existing deepfake detection approaches, our framework emphasizes high-level semantic attributes instead of focusing on particular visual anomalies. More specifically, it intrinsically decomposes facial representations into deepfake-relevant and unrelated components, leveraging only the deepfake-relevant features for classification between genuine and fabricated images. Furthermore, we introduce an adversarial mutual information minimization strategy that enhances the separability between these two types of information through decorrelation learning. This significantly improves the model's robustness to irrelevant variations and strengthens its generalization capability to previously unseen manipulation techniques. Extensive experiments demonstrate the effectiveness and superiority of our proposed DID framework for cross-dataset deepfake detection. It achieves an AUC of 0.779 in cross-dataset evaluation from FF++ to CDF2 and improves the state-of-the-art AUC significantly from 0.669 to 0.802 on the diffusion-based Text-to-Image dataset.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1659757</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1659757</link>
        <title><![CDATA[Towards the neuromorphic Cyber-Twin: an architecture for cognitive defense in digital twin ecosystems]]></title>
        <pubdate>2025-11-04T00:00:00Z</pubdate>
        <category>Conceptual Analysis</category>
        <author>Nida Nasir</author><author>Hussam Al Hamadi</author>
        <description><![CDATA[IntroductionAs cyber-physical systems become increasingly virtualized, digital twins have emerged as essential components for real-time monitoring, simulation, and control. However, their growing complexity and exposure to dynamic network environments make them vulnerable to sophisticated cyber threats. Traditional rule-based and machine-learning-based security models often fail to adapt in real time to evolving attack patterns, particularly in decentralized and resource-constrained settings.MethodsThis study introduces the Neuromorphic Cyber-Twin (NCT), a brain-inspired architectural framework that integrates spiking neural networks (SNNs) and event-driven cognition to enhance adaptive cyber defense. The NCT leverages neuromorphic principles such as sparse coding, temporal encoding, and spike-timing-dependent plasticity (STDP) to transform telemetry data from the digital-twin layer into spike-based sensory inputs. A layered cognitive architecture continuously monitors behavioral deviations, infers anomalies, and autonomously adapts its defensive responses in alignment with system dynamics.ResultsLightweight prototype simulations demonstrate the feasibility of NCT-based event-driven anomaly detection and adaptive defense. The results highlight advantages in low-latency detection, contextual awareness, and energy efficiency compared with conventional machine-learning models.DiscussionThe NCT framework represents a biologically inspired paradigm for scalable, self-evolving cybersecurity in virtualized ecosystems. Potential applications include infrastructure monitoring, autonomous transportation, and industrial control systems. Comprehensive benchmarking and large-scale validation are identified as future research directions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1600540</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1600540</link>
        <title><![CDATA[Research on fault-tolerant decision algorithm for data security automation]]></title>
        <pubdate>2025-10-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jianxin Li</author><author>Ruchun Jia</author><author>Ning Xiang</author><author>Yizhun Tian</author>
        <description><![CDATA[IntroductionTraditional operation and maintenance decision algorithms often ignore the analysis of data source security, making them highly susceptible to noise, time-consuming in execution, and lacking in rationality.MethodsIn this study, we design an automated operation and maintenance decision algorithm based on data source security analysis. A multi-angle learning algorithm is adopted to establish a noise data model, introduce relaxation variables, and compare sharing factors with noise data characteristics to determine whether the data source is secure. Taking the ideal power shortage and minimum maintenance cost as the objective function, we construct a classical particle swarm optimization model and derive the expressions for particle search velocity and position. To address the problem of local optima, a niche mechanism is incorporated: the obtained automated data is treated as the population, a reasonable number of iterations is determined, individual fitness is stored, and the optimal state is obtained through a continuous iterative update strategy.ResultsExperimental results show that the proposed strategy can shorten operation and maintenance time, enhance the rationality of decision-making, improve algorithm convergence, and avoid falling into local optima.DiscussionIn addition, fault-tolerant analysis is performed on data source security, effectively eliminating bad data, preventing interference from malicious data, and further improving convergence performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1581734</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1581734</link>
        <title><![CDATA[Domain-independent deception: a new taxonomy and linguistic analysis]]></title>
        <pubdate>2025-09-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rakesh M. Verma</author><author>Nachum Dershowitz</author><author>Victor Zeng</author><author>Dainis Boumber</author><author>Xuting Liu</author>
        <description><![CDATA[IntroductionInternet-based economies and societies are drowning in deceptive attacks. These attacks take many forms, such as fake news, phishing, and job scams, which we call “domains of deception.” Machine learning and natural language processing researchers have been attempting to ameliorate this precarious situation by designing domain-specific detectors. Only a few recent works have considered domain-independent deception. We collect these disparate threads of research and investigate domain-independent deception.MethodsFirst, we provide a new computational definition of deception and break down deception into a new taxonomy. Then, we briefly mention the debate on linguistic cues for deception. We build a new comprehensive real-world dataset for studying deception. We investigate common linguistic features for deception using both classical and deep learning models in a variety of situations including cross-domain experiments.ResultsWe find common linguistic cues for deception and give significant evidence for knowledge transfer across different forms of deception.DiscussionWe list several directions for future work based on our results.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1638307</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1638307</link>
        <title><![CDATA[Secure aggregation of sufficiently many private inputs]]></title>
        <pubdate>2025-09-10T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Thijs Veugen</author><author>Gabriele Spini</author><author>Frank Muller</author>
        <description><![CDATA[Secure aggregation of distributed inputs is a well-studied problem. In this study, anonymity of inputs is achieved by assuring a minimal quota before publishing the outcome. We design and implement an efficient cryptographic protocol that mitigates the most important security risks and show its application in the cyber threat intelligence (CTI) domain. Our approach allows for generic aggregation and quota functions. With 20 inputs from different parties, we can do three secure and anonymous aggregations per second, and in a CTI community of 100 partners, 10, 000 aggregations could be performed during one night.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1526480</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1526480</link>
        <title><![CDATA[Federated learning framework for IoT intrusion detection using tab transformer and nature-inspired hyperparameter optimization]]></title>
        <pubdate>2025-05-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohamed Abd Elaziz</author><author>Ibrahim A. Fares</author><author>Abdelghani Dahou</author><author>Mansour Shrahili</author>
        <description><![CDATA[Intrusion detection has been of prime concern in the Internet of Things (IoT) environment due to the rapid increase in cyber threats. Majority of traditional intrusion detection systems (IDSs) rely on centralized models, raising significant privacy concerns. Federated learning (FL) offers a decentralized alternative; however, many existing FL-based IDS frameworks suffer from poor performance due to suboptimal model architectures and ineffective hyperparameter selection. To address these challenges, this paper introduces a novel trust-centric FL framework based on the tab transformer (TTF) model for IDS. We enhance the Tab model through an optimization process, utilizing a hyperparameter tuning algorithm inspired by the nature-based electric eel foraging optimization (EEFO) algorithm. The goal of the developed framework is to improve the detection of IDS without using centralized data to preserve privacy. Whereas it enhances the processing and detection capability of huge amounts of data generated from IoT devices. Our framework is tested on three IoT datasets: N-BaIoT, UNSW-NB15, and CICIoT2023 to ensure the model's performance. Experimental results show that the proposed framework significantly exceeds traditional methods in terms of accuracy, precision, and recall. The results presented in this study confirm the effectiveness and superior performance of the proposed FL-based IDS framework.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1556157</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1556157</link>
        <title><![CDATA[Safeguarding digital livestock farming - a comprehensive cybersecurity roadmap for dairy and poultry industries]]></title>
        <pubdate>2025-04-16T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Suresh Neethirajan</author>
        <description><![CDATA[The rapid digital transformation of dairy and poultry farming through big data analytics and Internet of Things (IoT) innovations has significantly advanced precision management of feeding, animal health, and environmental conditions. However, this digitization has simultaneously escalated cybersecurity vulnerabilities, presenting serious threats to economic stability, animal welfare, and food safety. This paper provides an in-depth analysis of the evolving cyber threat landscape confronting digital livestock farming, examining ransomware incidents, hacktivist interference, and state-sponsored cyber intrusions. It critically assesses how compromised digital systems disrupt critical farm operations, including milking routines, feed formulations, and climate control, profoundly impacting animal health, productivity, and consumer trust. Responding to these challenges, we present a comprehensive cybersecurity roadmap that integrates established IT security practices with agriculture-specific requirements. The roadmap emphasizes advanced solutions, such as AI-driven anomaly detection, blockchain-based traceability, and integrated cybersecurity-biosecurity frameworks, tailored explicitly to safeguard livestock farming. Additionally, we highlight human-centric elements such as targeted workforce education, rural cybersecurity capacity building, and robust cross-sector collaboration as indispensable components of a resilient cybersecurity ecosystem. By synthesizing technical advancements, regulatory perspectives, and socio-economic insights, the paper proposes a proactive strategy to enhance data integrity, secure animal welfare, and reinforce food supply chains. Ultimately, we underscore that effective cybersecurity is not merely a technical consideration but foundational to ensuring the sustainable, ethical, and trustworthy advancement of livestock agriculture in a data-driven world.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1532362</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1532362</link>
        <title><![CDATA[Big data and personal information privacy in developing countries: insights from Kenya]]></title>
        <pubdate>2025-04-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Johnson Masinde</author><author>Franklin Mugambi</author><author>Daniel Wambiri Muthee</author>
        <description><![CDATA[The present study examined the correlation between big data and personal information privacy in Kenya, a developing nation which has experienced a significant rise in utilization of data in the recent past. The study sought to assess the effectiveness of present data protection laws and policies, highlight challenges that individuals and organizations experience while securing their data, and propose mechanisms to enhance data protection frameworks and raise public awareness of data privacy issues. The study employed a mixed-methods approach, which included a survey of 500 participants, 20 interviews with key stakeholders, and an examination of 50 pertinent documents. Study findings show that the regulatory and legal frameworks though present are not enforced, demonstrating a gap between legislation and implementation. Furthermore, there is a lack of understanding about the risks posed by sharing personal information, and that more public education and awareness activities are required. The findings also demonstrate that while people are prepared to trade their personal information for concrete benefits, they are concerned about how their data is utilized and by whom. The study proposes the establishment of a National Data Literacy Training and Capacity Building Framework (NADACA), that should mandate the training of government officials in best practices for data governance and enforcement mechanisms, educate the public on personal data privacy and relevant laws, and ensure the integration of data literacy into the curriculum, alongside the provision of regular resources and workshops on data literacy. The study has significant implications for policymakers, industry representatives, and civil society organizations in Kenya and globally.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1513027</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1513027</link>
        <title><![CDATA[Big data analytics and AI as success factors for online video streaming platforms]]></title>
        <pubdate>2025-02-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhammad Arshad</author><author>Choo Wou Onn</author><author>Ashfaq Ahmad</author><author>Goabaone Mogwe</author>
        <description><![CDATA[As the trend in the current generation with the use of mobile devices is rapidly increasing, online video streaming has risen to the top in the entertainment industry. These platforms have experienced radical expansion due to the incorporation of Big Data Analytics and Artificial Intelligence which are critical in improving the user interface, improving its functioning, and customization of recommended content. This paper seeks to examine how Big Data Analytics makes it possible to obtain large amounts of data about users and how they view, what they like, or how they behave. While customers benefit from this data by receiving more suitable material, getting better recommendations, and allowing for more efficient content delivery, AI utilizes it. As a result, the study also points to the importance and relevance of such technologies to promote business development, and user interaction and maintain competitiveness in the online video streaming market with examples of their effective application. This work presents a comprehensive investigation of the combined role of Big Data and AI and presents the necessary findings to determine their efficacy as success factors of existing and future video streaming services.]]></description>
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