Search Results (138)

This paper presents a pilot study on cross-device variability in online signature dynamics captured on consumer Samsung devices using S-Pen technology. Signature data were acquired on two devices, a Galaxy Ultra smartphone and a Galaxy Tab S6 Lite tablet, through a unified web-based interface designed to ensure consistent capture across platforms. The acquisition process recorded timestamped x–y trajectories, stroke events, and pressure information when available, preserving temporal structure for dynamic analysis. Genuine signatures were systematically divided into reference and test sets, and comparisons were performed under intra-device conditions (enrollment and verification on the same device) and cross-device conditions (enrollment and verification on different devices). Similarity was evaluated using Dynamic Time Warping (DTW) on multivariate time series, with analysis focused on how differences in form factor and writing area influence signature behavior. This problem is directly relevant to mobile biometric authentication workflows, where users frequently enroll on one device and later verify on another; under this mismatch scenario, reduced separability between genuine and impostor scores can affect decision reliability. Consistent with this interpretation, the results show lower dissimilarity in intra-device comparisons and higher distances with ROC degradation under cross-device mismatch. These findings provide exploratory evidence that device heterogeneity is a practical factor in mobile signature verification and support the need for cross-device-aware design in authentication systems used for digital transactions and document authorization in real-world mobile environments. Full article

It is well-known that GNSS high accuracy solutions are increasingly vulnerable to jamming and spoofing attacks, posing significant challenges to their reliability, security, and accuracy. In the past years, GNSS communities have witnessed an increase in the frequency and sophistication of these attacks, driven, among other factors, by the widespread availability of low-cost, off-the-shelf equipment capable of denying or even totally misleading GNSS-based positioning systems. On the one hand, jamming attacks aim at inhibiting signal reception by introducing high-power noise or interference, leading to degraded performance or complete failure in determining position. Jamming detection mechanisms need to be traced to GNSS receiver mitigation measures at signal processing level to analyze the radio frequency (RF) environment or receiver behavior. Signal-to-noise ratio (SNR) monitoring, power spectrum analysis, and signal power monitoring are commonly used to detect anomalies in signal characteristics. Jamming is often indicated with the presence of a combination of one or more dedicated indicators, opening space to characterize different levels of jamming attack allowing to optimize a response at user level. On the other hand, detecting spoofing attacks requires different advanced techniques to identify anomalies in satellite signals, receiver behavior, or consistency of computed position data. Indicators regarding internal consistency checks, as well as unexpected evolutions of GNSS signals, are typically suspicious behaviors to be analyzed as possible attacks. Additionally, ensuring trust in the received navigation information by including cryptographic authentication mechanisms is key to quickly detecting some kinds of spoofing. This paper presents the latest enhancements on jamming and spoofing detection and mitigation mechanisms for GMV GSharp^® high accuracy and safe positioning solution. This new method, based on fuzzy logic systems, allows us to distinguish between different levels of attack and adapt the reactions to reduce the impact on the final user as much as possible. Additionally, test results obtained from real GNSS attacks datasets will be shown. Full article

With the improvement of computing power in terminal devices and their widespread application in emerging technology fields, ensuring secure access to terminals has become an important challenge in the current network environment. Traditional security authentication and trust evaluation methods have many shortcomings in dealing with dynamic and complex network environments, such as limited ability to respond to new threats and inability to adjust evaluation strategies in real time. In response to these issues, this article proposes a dynamic weighted authentication trust evaluation method driven by multimodal data under zero trust architecture. The method introduces user operation risk values and time coefficients, which can dynamically reflect the behavior changes of users and devices in different times and environments, achieving more flexible and accurate trust evaluation. In order to further improve the accuracy of the evaluation, this article also uses the dynamic entropy weight method to calculate the weights of the evaluation indicators. By coupling with the evaluation values, the terminal access security authentication trust score is obtained, and the current authentication trust level is determined to ensure the overall balance of the trust evaluation results. The experimental results show that compared with traditional evaluation algorithms based on information entropy and collaborative reputation, the average error of the method proposed in this study has been reduced by 87.5% and 75%, respectively. It has significant advantages in dealing with complex network attacks, reducing security vulnerabilities, and improving system adaptability. Full article

Mobile authentication is crucial for device security; however, conventional techniques such as PINs and swipe patterns are susceptible to social engineering attacks. This work explores the integration of touch stroke analysis and Explainable AI (xAI) to address these vulnerabilities. Unlike static methods that require intervention at specific intervals, continuous authentication offers dynamic security by utilizing distinct user touch dynamics. This study aggregates touch stroke data from 150 participants to create comprehensive user profiles, incorporating novel biometric features such as mid-stroke pressure and mid-stroke area. These profiles are analyzed using machine learning methods, where the Random Tree classifier achieved the highest accuracy of 97.07%. To enhance interpretability and user trust, xAI methods such as SHAP and LIME are employed to provide transparency into the models’ decision-making processes, demonstrating how integrating touch stroke dynamics with xAI produces a visible, trustworthy, and continuous authentication system. Full article

With the rapid development of online aquatic product trading, traditional centralized platforms are facing increasing pressure in terms of data security, privacy protection, and trust. Problems such as tampering with transaction records, weak identity authentication, privacy leakage, and the difficulty of balancing matching efficiency with security limit the further development of these platforms. To address these issues, this paper proposes a blockchain-based identity authentication and access control scheme for online aquatic product trading. The scheme first introduces a dual authentication mechanism that combines a verifiable random function with a Schnorr-based zero-knowledge proof, providing strong decentralized identity verification and resistance to replay attacks. It then designs a dynamic access control strategy based on a multi-dimensional reputation model, which converts user behavior, attributes, and historical transaction performance into a comprehensive trust score used to determine fine-grained access rights. In addition, an AES-PEKS hybrid encryption method is employed to support encrypted keyword search and order matching while protecting the confidentiality of order data. This paper implements a multi-channel architecture for aquatic product trading prototype system on Hyperledger Fabric. This system separates registration, order processing, and reputation management into different channels to improve concurrency and enhance privacy protection. Security analysis shows that the proposed solution effectively defends against replay attacks, key leaks, data tampering, and privacy theft. Performance evaluation further demonstrates that, compared to a single-chain architecture, the multi-channel design, while increasing security mechanisms, maintains a stable throughput of approximately 223 tx/s even when concurrency reaches 600–800 tx/s, ensuring normal operation of the trading system. These results indicate that this solution provides a practical technical approach and system-level reference for building secure, reliable, and efficient online aquatic product trading platforms. Full article

In the realm of wearable technology, achieving robust continuous authentication requires balancing high security with the strict resource constraints of embedded platforms. Conventional machine learning approaches and deep learning-based biometrics often incur high computational costs, making them unsuitable for low-power edge devices. To address this challenge, we propose H-PPG, a lightweight authentication system that integrates photoplethysmography (PPG) and inertial measurement unit (IMU) signals for continuous user verification. Using Hyperdimensional Computing (HDC), a lightweight classification framework inspired by brain-like computing, H-PPG encodes user physiological and motion data into high-dimensional hypervectors that comprehensively represent individual identity, enabling robust, efficient and lightweight authentication. An adaptive learning process is employed to iteratively refine the user’s hypervector, allowing it to progressively capture discriminative information from physiological and behavioral samples. To further enhance identity representation, a dimension regeneration mechanism is introduced to maximize the information capacity of each dimension within the hypervector, ensuring that authentication accuracy is maintained under lightweight conditions. In addition, a user-defined security level scheme and an adaptive update strategy are proposed to ensure sustained authentication performance over prolonged usage. A wrist-worn prototype was developed to evaluate the effectiveness of the proposed approach and extensive experiments involving 15 participants were conducted under real-world conditions. The experimental results demonstrate that H-PPG achieves an average authentication accuracy of 93.5%. Compared to existing methods, H-PPG offers a lightweight and hardware-efficient solution suitable for resource-constrained wearable devices, highlighting its strong potential for integration into future smart wearable ecosystems. Full article

Impersonation attacks causing online fraud are a growing challenge for digital services, demanding the integration of biometric and behavioral factors into traditional authentication methods. Behavioral impersonation detection during online sessions is particularly critical for online banking, and the existing solutions focus mostly on mouse and keyboard dynamics. We study behavioral patterns extracted from standard web-server logs and claim that our methods are applicable in a banking scenario. Using a Siamese neural network, we classify pairs of web sessions from the same user with 90% accuracy. Experiments conducted on real-world intranet weblogs, serving as a proxy for banking data, highlight challenges in filtering and aggregating data. To address variability in website technologies and browsing behaviors, we introduce an automated procedure for grouping requested pages based on a low-rank approximation of the user browsing matrix. This approach consistently improves classification accuracy while reducing reliance on costly, error-prone manual log analysis, offering a scalable, viable approach for fraud detection in online services. Full article

This study examines digital sustainability signals and the psychological mechanisms (authenticity and trust) that relate to consumers’ sustainable food purchase intentions. While the attitude–behavior gap remains a persistent challenge in sustainability research, our study focuses on upstream factors that may help explain why intentions vary in strength. Drawing on signaling theory, this research develops and tests a framework that combines positive signals (e.g., influencer credibility) and negative signals (e.g., perceived greenwashing) to investigate the impact on green brand authenticity, brand trust, and purchase intention. Data were gathered from a survey of 324 adult social media users who follow influencers with a focus on sustainability and have recent experience buying eco-labeled food products. Using PLS-SEM, results indicate that influencer credibility has a significant and positive effect on perceptions of green brand authenticity, whereas the influence of greenwashing has a significant and negative effect. Authenticity shows a strong prediction of brand trust, and this in turn predicts green purchase intentions with trust mediating the authenticity–intention relationship to some degree. The results indicate authenticity as a key mechanism by which digital signals affect sustainable consumption. The research provides practical insights for food brands seeking to strengthen the psychological conditions that support sustainable consumption intentions. Full article

With the development of information technology, live shopping has emerged as a new approach to product marketing and has attracted considerable attention. However, in the context of an aging population, little is known about the factors influencing the intention of the elderly to engage in live shopping. The aim of this study is to determine the psychological and cognitive mechanisms that influence the willingness of elderly people to engage in live shopping. This study integrated the Flow Theory and the Information System Success Model to construct a live shopping acceptance model for the elderly based on the Stimulus–Organism–Response model. It was used for in-depth insight into the live shopping behaviors of elderly users. The structural equation model was used in the study to analyze 337 valid questionnaires. The results showed that interactivity, authenticity, attractiveness, and entertainment could improve the flow in livestreaming shopping among elderly users. Entertainment and attractiveness had a positive influence on perceived pleasure, and flow in live shopping, and perceived pleasure had a direct and significant influence on the elderly’s intention to make a live purchase. The factors of information quality and ease of use have no direct impact on perceived pleasure. This study enriched the user behavior theory of live shopping and provided inspiration for the aging-friendly and sustainable development of live shopping services of shopping platforms, live streamers, and service providers. Full article

Score fusion is a technique that combines the matching scores from multiple biometric modalities for an authentication system. Biometric modalities are unique physical or behavioral characteristics that can be used to identify individuals. Biometric authentication systems use these modalities to verify or identify individuals. Score fusion can improve the performance of biometric authentication systems by exploiting the complementary strengths of different modalities and reducing the impact of noise and outliers from individual modalities. This paper proposes a new score fusion method based on the Sine Cosine Algorithm (SCA). SCA is a meta-heuristic optimization algorithm used in various optimization problems. The proposed method extracts features from multiple biometric sources and then computes intra/inter scores for each modality. The proposed method then normalizes the scores for a given user using different biometric modalities. Then, the mean, maximum, minimum, median, summation, and Tanh are used to aggregate the scores from different biometric modalities. The role of the SCA is to find the optimal parameters to fuse the normalized scores. We evaluated our methods on the CASIA-V3-Internal iris dataset and the AT&T (ORL) face database. The proposed method outperforms existing optimization-based methods under identical experimental conditions and achieves an Equal Error Rate (EER) of 1.003% when fusing left iris, right iris, and face. This represents an improvement of up to 85.89% over unimodal baselines. These findings validate SCA’s effectiveness for adaptive score fusion in multimodal biometric systems. Full article

Continuous and personalized monitoring are beneficial for patients suffering from neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease and multiple sclerosis. However, such levels of monitoring are seldom ensured by traditional models of care. This paper presents NeuroPredict, a secure edge–cloud Internet of Medical Things (IoMT) platform that addresses this problem by integrating commercial wearables and in-house sensors with cognitive and behavioral evaluations. The NeuroPredict platform links high-frequency physiological signals with periodic cognitive tests through the use of a modular architecture with lightweight device connectivity, a semantic integration layer for timestamp alignment and feature harmonization across heterogeneous streams, and multi-timescale data fusion. Its use of encrypted transport and storage, role-based access control, token-based authentication, identifier separation, and GDPR-aligned governance addresses security and privacy concerns. Moreover, the platform’s user interface was built by considering human-centered design principles and includes role-specific dashboards, alerts, and patient-facing summaries that are meant to encourage engagement and decision-making for patients and healthcare providers. Experimental evaluation demonstrated the NeuroPredict platform’s data acquisition reliability, coherence in multimodal synchronization, and correctness in role-based personalization and reporting. The NeuroPredict platform provides a smart system infrastructure for eHealth and remote monitoring in neurodegenerative care, aligned with priorities on wearables/IoMT integration, data security and privacy, interoperability, and human-centered design. Full article

In the current digital landscape, where platforms process AI-generated content and intelligent network traffic on a large scale, it is the duty of such platforms to continuously measure the reliability, trustworthiness, and security of various data streams. Driven by this practical challenge, this research develops an effective decision-support mechanism in intelligent decision-making in big-data AI-generated content and network systems. The decision problem has considered several uncertainties, including content authenticity, processing efficiency, user trust, cybersecurity, system scalability, privacy protection, and cost of computing. The multidimensional uncertainty of AI-generated information and trends in network behavior are challenging to capture in traditional crisp and fuzzy decision-making models. To fill that gap, a new Picture Fuzzy Faire Un Choix Adequat (PF-FUCA) methodology is proposed, based on multi-perspective expert assessment and better computational aggregation to improve the accuracy of rankings, symmetry, and uncertainty treatment. A case scenario comprising fifteen different alternative intelligent decision strategies and seven evaluation criteria are examined under the evaluation of four decision-makers. The PF-FUCA model successfully prioritizes the best strategies to control AI-based content and network activities to generate a stable and realistic ranking. The comparative and sensitivity analysis show higher robustness, accuracy, and flexibility levels than the existing MCDM techniques. The results indicate that PF-FUCA is specifically beneficial in settings where a large amount of data has to flow, a high uncertainty rate exists, and the variables of decision are dynamic. The research introduces a scalable and credible methodological conception that can be used to facilitate high levels of intelligent computing applications to content governance and network optimization. Full article

Social networks have emerged as integral platforms for communication and information dissemination in contemporary society. The spread of negative sentiments and its impact on activities of users in social networks is a crucial issue. When users receive negative reviews about news or articles, regardless of authenticity, they form opinions based on their own understanding, and statistics show that more than 90% of the time this reveals predictable behavior patterns. To address this situation, the proposed Antecedents of Negative Sentiment through Expression Capacity: Mixed Methods (ANSEC-MM) study identifies the antecedents of negative sentiment using expression capacity as a mixed-methods approach to mitigate the generation of negative sentiments. The proposed model introduces the concept of identification of influencer nodes with further categorization into active and inactive influencer nodes. The model separates negative influencer nodes from positive nodes and processes the negative influencer nodes further. A Node Expressive Capacity (NE) metric predicts the frequency with which users interact with neighboring influencer nodes, which contributes to the generation of negative sentiments. A Cognitive Effect Coefficient (φ) defines the temperament status of the users. Through further computation, the model distinguishes the proportion of negative sentiments from positive ones. Negative sentiment mitigation is achieved through a developed algorithmic approach. Performance is tested and compared across three datasets against state-of-the-art models: EANN, BERT, and AOAN. The proposed model demonstrated superior performance in negative sentiment detection and mitigation, achieving accuracy rates of 90% and 88%, respectively, compared to existing models. Full article

The rise of social media has democratized information sharing, allowing ordinary individuals to become influential voices in public discourse. However, traditional methods for identifying influential users rely primarily on network centrality measures that fail to capture the behavioral dynamics underlying actual influence capacity in digital environments. This study introduces the Social Influence Strength Index (SISI), a metric grounded in social impact theory that assesses influence through behavioral engagement indicators rather than network structure alone. The SISI combines three key elements: the average engagement rate, follower reach score, and mention prominence score, using a geometric mean to account for the multiplicative nature of social influence. This was developed and validated using a dataset of 1.2 million tweets from South African migration discussions, a context characterized by high emotional engagement and diverse participant types. SISI’s behavioral principles make it applicable for identifying influential voices across various social media contexts where authentic engagement matters. The results demonstrate substantial divergence between SISI and traditional centrality measures (Spearman ρ = 0.34, 95% CI: 0.32–0.36 with eigenvector centrality; top-10 user overlap Jaccard index = 0.20), with the SISI consistently recognizing behaviorally influential users that network-based approaches overlook. Validation analyses confirm the SISI’s predictive validity (high-SISI users maintain 3.5× higher engagement rates in subsequent periods, p < 0.001), discriminant validity (distinguishing content creators from amplifiers, Cohen’s d = 1.32), and convergent validity with expert assessments (Spearman ρ = 0.61 vs. ρ = 0.28 for eigenvector centrality). The research reveals that digital influence stems from genuine audience engagement and community recognition rather than structural network positioning. By integrating social science theory with computational methods, this work presents a theoretically grounded framework for measuring digital influence, with potential applications in understanding information credibility, audience mobilization, and the evolving dynamics of social media-driven public discourse across diverse domains including marketing, policy communication, and digital information ecosystems. Full article

One-Time Passwords (OTPs) are a core component of multi-factor authentication in banking, e-commerce, and digital platforms. However, conventional delivery channels such as SMS and email are increasingly vulnerable to SIM-swap fraud, phishing, spoofing, and session hijacking. This study proposes an end-to-end mobile authentication architecture that integrates a permissioned Hyperledger Fabric blockchain for tamper-evident identity management, an AI-driven risk engine for behavioral and SIM-swap anomaly detection, Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification, and geolocation-bound OTP validation for contextual assurance. Hyperledger Fabric is selected for its permissioned governance, configurable endorsement policies, and deterministic chaincode execution, which together support regulatory compliance and high throughput without the overhead of cryptocurrency. The system is implemented as a set of modular microservices that combine encrypted off-chain storage with on-chain hash references and smart-contract–enforced policies for geofencing and privacy protection. Experimental results show sub-0.5 s total verification latency (including ZKP overhead), approximately 850 transactions per second throughput under an OR-endorsement policy, and an F1-score of 0.88 for SIM-swap detection. Collectively, these findings demonstrate a scalable, privacy-centric, and interoperable solution that strengthens OTP-based authentication while preserving user confidentiality, operational transparency, and regulatory compliance across mobile network operators. Full article