Search Results (125)

Post-quantum cryptographic implementations in Internet-of-Things (IoT) devices are significantly threatened by physical side-channel attacks, where practical attack risks are increased by physical accessibility and resource limitations. In particular, recent work has shown that belief propagation-based attacks can recover secret keys from lattice-based digital signatures using only a single side-channel trace of the Number Theoretic Transform (NTT). This work introduces the Quantum-Randomized Number Theoretic Transform (QR-NTT), an implementation-level defense mechanism that integrates quantum-derived entropy directly into the execution flow of lattice-based signature algorithms. Rather than treating randomness as a static input, QR-NTT uses quantum entropy to introduce controlled variability in execution ordering, arithmetic factor usage, and memory access behavior while preserving mathematical correctness and constant-time execution. The proposed framework is designed for embedded platforms and remains compatible with existing post-quantum cryptographic standards and IoT communication protocols. A complete implementation on an ARM Cortex-M4 platform, coupled with commercial quantum random number generator (QRNG) hardware, demonstrates that QR-NTT significantly degrades the effectiveness of template matching and belief propagation attacks. Experimental evaluation shows a reduction in single-trace attack success rates from over 90% to below 3% and an increase of approximately two orders of magnitude in the number of traces required for successful key recovery. These security gains are achieved with moderate overheads of 18.3% in execution time and 1.8 KB of additional memory while remaining well within practical IoT constraints. The results indicate that quantum-derived entropy can be leveraged as a practical implementation-level defense against physical attacks, complementing algorithmic post-quantum security. QR-NTT demonstrates a viable path toward strengthening the real-world resilience of post-quantum IoT systems without sacrificing deployability. Full article

Social media has become a prominent digital environment associated with adolescents’ food preferences and the environmental impacts of their diets. This study aimed to examine the relationship between social media usage habits, food choice motivations, and the environmental impact of the diet, specifically the carbon footprint, in adolescents. This cross-sectional study was conducted with 216 adolescents aged 14–18 years in Istanbul between January and April 2025. Data were collected using the Food Choice Questionnaire (FCQ) and a 24 h dietary recall. The dietary carbon footprint was calculated by mapping 24 h dietary recall data to emission factors from the Data FIELDS database and scientific literature. Of the participants, 60.6% were female. Females had significantly higher rates of being influenced by social media in food choices (p < 0.001) and total FCQ scores (p = 0.025) compared to males. Regarding social media platforms, TikTok usage was associated with higher ethical concern and mood scores (p < 0.001), while Instagram usage was associated with weight control (p = 0.012). Daily internet use of 180 min was associated with higher price (p = 0.001) and weight control (p = 0.003) motivations. Notably, a significant negative correlation was found between health motivation and carbon footprint (r = −0.173, p = 0.011). Multivariate regression analysis confirmed that an increase in health score was associated with a reduction in carbon footprint (β = −0.204, p = 0.003), independent of gender, BMI, and social media influence. Social media platforms serve as a relevant digital environment associated with adolescents’ food preferences. The finding that health-oriented choices are associated with lower carbon footprints indicates that promoting healthy eating on social media will benefit both individual and planetary health. Full article

Fast large-scale network scanning is an important way to understand internet service configurations and security in real time, among which stateless scan technology is representative. Existing stateless scanners can perform single-packet scans for internet-wide network measurements but are limited to host discovery or port scanning. To obtain further information over TCP, slower stateful scanners must be used in conjunction, which spend more time and memory because of connection state maintenance. Through the simplification of the TCP finite state machine (FSM), this paper proposes a novel stateless scanning model, which can establish TCP connections and obtain further responses in a completely stateless manner. Based on this model, we implement ZBanner, an improved modular stateless scanner that utilizes user-defined probes for identifying services and versions, fingerprinting TLS servers, etc. We present the unique design of ZBanner and experimentally characterize its feasibility and performance. Experiments show that ZBanner performs better than current state-of-the-art solutions in terms of scan rate and memory usage. ZBanner achieves a scan rate that is at least three times faster than current tools for generic ports and over 90 times faster for open ports while keeping a minimum and stable memory usage. Full article

Background/Objectives: The frequent and uncontrolled use of digital devices has resulted in phenomena such as technological addiction and problematic ICT use, especially after the pandemic. This has been associated with several factors related to psychological distress in young adults, but less is known about the subject in adolescents. Thus, the aim of this study is to analyze the relationship between problematic Internet use and psychological distress factors in high school students and examine whether these variables differed when gender and academic grade level were considered. Methods: A quantitative, cross-sectional, and descriptive–correlational study was employed. A total of 2048 students from the Basque Country, aged between 11 and 17 years old, completed an online self-report questionnaire composed of demographics and ad hoc items, the Problematic Internet Use Scale (PIUS), and selected subscales from the Child and Adolescent Assessment System (anxiety, social anxiety, and depression). Descriptive statistics, Pearson correlation analyses, independent-sample ANOVA, Bonferroni post hoc tests, and independent-sample t tests were conducted. Results: Even though no differences were found between males and females when PIUS was analyzed, significant differences were found between students from different academic grade levels, whereby those from higher levels presented higher rates of problematic Internet use. Significant correlations were found between PIUS and depression, anxiety, and social anxiety. Furthermore, those who showed more problematic Internet use also presented higher anxiety, social anxiety, and depression levels. Conclusions: Adolescents in higher grade levels tend to exhibit a higher incidence of problematic Internet use. Consequently, intensive and uncontrolled Internet usage has been linked to poorer mental health. The findings underline the importance of promoting digital literacy among adolescents. These results highlight the importance of approaching psychological distress through prevention initiatives and emphasize the protective role that both schools and families play in promoting healthier and more balanced Internet use among adolescents. Full article

The rapid expansion of the Industrial Internet of Things (IIoT) within smart grid infrastructures has increased the risk of sophisticated cyberattacks, where severe class imbalance and stringent real-time requirements continue to hinder the effectiveness of conventional intrusion detection systems (IDSs). Existing approaches often achieve high accuracy on specific datasets but lack generalizability, interpretability, and stability when deployed across heterogeneous IIoT environments. This paper introduces AegisGuard, a hybrid intrusion detection framework that integrates an adaptive four-stage sampling process with a calibrated ensemble learning strategy. The sampling module dynamically combines SMOTE, SMOTE-ENN, ADASYN, and controlled under sampling to mitigate the extreme imbalance between benign and malicious traffic. A quantum-inspired feature selection mechanism then fuses statistical, informational, and model-based significance measures through a trust-aware weighting scheme to retain only the most discriminative attributes. The optimized ensemble, comprising Random Forest, Extra Trees, LightGBM, XGBoost, and CatBoost, undergoes Optuna-based hyperparameter tuning and post-training probability calibration to minimize false alarms while preserving accuracy. Experimental evaluation on four benchmark datasets demonstrates the robustness and scalability of AegisGuard. On the CIC-IoT 2023 dataset, it achieves 99.6% accuracy and a false alarm rate of 0.31%, while maintaining comparable performance on TON-IoT (98.3%), UNSW-NB15 (98.4%), and Bot-IoT (99.4%). The proposed framework reduces feature dimensionality by 54% and memory usage by 65%, enabling near real-time inference (0.42 s per sample) suitable for operational IIoT environments. Full article

In the last few decades, several wearable devices have been designed to monitor respiration rate to capture pulmonary signals with a higher accuracy and reduce patients’ discomfort during use. In this article, we present the design and implementation of a device for the real-time monitoring of respiratory system movements. When breathing, the circumference of the abdomen and thorax changes; therefore, we used a Force-Sensing Resistor (FSR) attached to a Printed Circuit Board (PCB) to measure this variation as the patient inhales and exhales. The mechanical strain this causes changes the FSR electrical resistance accordingly. Also, for streaming this variable resistance on an Internet of Things (IoT) platform, Bluetooth Low Energy (BLE) 5 is utilized due to its adequate throughput, high accessibility, and the possibility of power consumption reduction. In addition to the sensing mechanism, the device includes a compact, energy-efficient micro-controller and a three-axis accelerometer that captures body movement. Power is supplied by a rechargeable Lithium-ion Polymer (LiPo) battery, and energy usage is optimized using a buck converter. For comfort and usability, the enclosure was 3D printed using Stereolithography (SLA) technology to ensure a smooth, ergonomic shape. This setup allows the device to operate reliably over long periods without disturbing the user. Altogether, the design supports continuous respiratory tracking in both clinical and home settings, offering a practical, low-power, and portable solution. Full article

The widespread integration of Internet-connected devices into industrial environments has enhanced connectivity and automation but has also increased the exposure of industrial cyber–physical systems to security threats. Detecting anomalies is essential for ensuring operational continuity and safeguarding critical assets, yet the dynamic, real-time nature of such data poses challenges for developing effective defenses. This paper introduces DataSense, a comprehensive dataset designed to advance security research in industrial networked environments. DataSense contains synchronized sensor and network stream data, capturing interactions among diverse industrial sensors, commonly used connected devices, and network equipment, enabling vulnerability studies across heterogeneous industrial setups. The dataset was generated through the controlled execution of 50 realistic attacks spanning seven major categories: reconnaissance, denial of service, distributed denial of service, web exploitation, man-in-the-middle, brute force, and malware. This process produced a balanced mix of benign and malicious traffic that reflects real-world conditions. To enhance its utility, we introduce an original feature selection approach that identifies features most relevant to improving detection rates while minimizing resource usage. Comprehensive experiments with a broad spectrum of machine learning and deep learning models validate the dataset’s applicability, making DataSense a valuable resource for developing robust systems for detecting anomalies and preventing intrusions in real time within industrial environments. Full article

With the exponential growth of Internet of Things (IoT) and wearable devices for home automation and industrial applications, vast volumes of data are continuously generated, requiring efficient data collection methods. IoT devices, being resource-constrained and typically battery-dependent, require lightweight protocols that optimize resource usage and energy consumption. Among such IoT devices, this article focuses on Bluetooth-based beacons due to their low latency and the advantage of not requiring pairing for communications. Specifically, to tackle the limitations of beacons in terms of bandwidth and transmission frequency, this article proposes a protocol that modifies beacon frames to include up to three parameters per frame and that allows for making use of configurable beaconing intervals based on the specific requirements of the communications scenario. Moreover, the use of the proposed protocol leads to increased data rates for beaconing transmissions, providing a low latency and a flexible configuration that permits adjusting different parameters. The proposed solution enables end-to-end interoperability in Opportunistic Edge Computing (OEC) networks by integrating a lightweight bridge module to transparently manage BLE advertisement segments. To demonstrate the performance of the devised opportunistic protocol, it is evaluated across multiple scenarios (i.e., in a short-distance reference scenario, inside a home with diverse obstacles, inside a building, outdoors and in an industrial scenario), showing its flexibility and ability to collect substantial data volumes from heterogeneous IoT devices. Full article

Background: China is confronted with the dual challenges of deeply interwoven population aging and the digitalization process. The digital integration and mental health issues of the elderly group are becoming increasingly prominent. Objectives: The present study aimed to analyze the pathways through which individual, family, and social factors influence Internet use in the elderly through a multi-level analysis framework, to examine the association between Internet use and mental health with a view to providing empirical evidence for digital technology-based mental health intervention programs for the elderly, and to promote the scientific practice of the goal of healthy aging. Methods: Based on the data of the 2021 China General Social Survey (CGSS) and provincial Internet development indicators, a mixed cross-sectional dataset was constructed. Logistic hierarchical regression and OLS regression methods were adopted to systematically investigate the multi-level factors associated with Internet use among the elderly group and its association with mental health. Results: The results indicate that individual resources (younger age, higher education level, and good health status) and family technical support (family members’ Internet access) are strongly associated with Internet usage among the elderly, while regional Internet penetration rate appears to operate indirectly through micro-mechanisms. Analysis of the association with mental health showed that Internet use was related to a lower score of depressive tendency (p < 0.05), and this association remained robust after controlling for variables at the individual, family, and social levels. Conclusions: The research results provide empirical evidence for the health promotion policies for the elderly, advocating the construction of a collaborative intervention framework of “individual ability improvement–intergenerational family support–social adaptation for the elderly” to bridge the digital divide and promote the digital integration of the elderly population in China. Full article

Smartphones have revolutionized our daily lives, becoming portable pocket computers with easy internet access. India, the second-highest smartphone and internet user, experienced a significant rise in smartphone usage between 2013 and 2024. Prolonged smartphone use, exceeding 20 min at a time, can lead to physical and mental health issues, including psychophysiological disorders. Digital devices and their extended exposure to blue light cause digital eyestrain, sleep disorders and visual-related problems. This research examines the impact of 1 h smartphone usage on visual fatigue among young Indian adults. A portable, low-cost system has been developed to measure visual activity to address this. The developed visual activity measurement system measures blink rate, inter-blink interval, and pupil diameter. Measured eye activity was recorded during 1 h smartphone usage of e-book reading, video watching, and social-media reels (short videos). Social media reels show increased screen variations, affecting pupil dilation and reducing blink rate due to continuous screen brightness and intensity changes. This reduction in blink rate and increase in inter-blink interval or pupil dilation could lead to visual fatigue. Full article

In an era where digitalization shapes economic and social landscapes, the intersection of digital skills, ethics, and integrity plays a crucial role in understanding the vulnerability of youth classified as NEET (Not in Education, Employment, or Training). This study explores how risky internet use and digital skill gaps contribute to socio-economic exclusion, integrating a multivariate and spatial approach to assess regional disparities in Europe. This study adopts a systems thinking perspective to explore digital exclusion as an emergent outcome of multiple interrelated subsystems. The research employs logistic regression, Principal Component Analysis (PCA) with Promax rotation, and Geographic Information Systems (GIS) to examine the impact of digital behaviors on NEET status. Using Eurostat data aggregated at the country level for the period (2000–2023) across 28 European countries, this study evaluates 24 digital indicators covering social media usage, instant messaging, daily internet access, data protection awareness, and digital literacy levels. The findings reveal that low digital skills significantly increase the likelihood of being NEET, while excessive social media and internet use show mixed effects depending on socio-economic context. A strong negative correlation between digital security practices and NEET status suggests that youths with a higher awareness of online risks are less prone to socio-economic exclusion. The GIS analysis highlights regional disparities, where countries with limited digital access and lower literacy levels exhibit higher NEET rates. Digital exclusion is not merely a technological issue but a multidimensional socio-economic challenge. To reduce the NEET rate, policies must focus on enhancing digital skills, fostering online security awareness, and addressing regional disparities. Integrating GIS methods allows for the identification of territorial clusters with heightened digital vulnerabilities, guiding targeted interventions for improving youth employability in the digital economy. Full article

Novel networking paradigms such as the Internet of Things (IoT) have expanded their usage and deployment to various application domains. Consequently, unseen critical security vulnerabilities such as zero-day attacks have emerged in such deployments. The design of intrusion detection systems for IoT networks is often challenged by a lack of labeled data, which complicates the development of robust defenses against adversarial attacks. As deep learning-based network intrusion detection systems, network intrusion detection systems (NIDS) have been used to counteract emerging security vulnerabilities. However, the deep learning models used in such NIDS are vulnerable to adversarial examples. Adversarial examples are specifically engineered samples tailored to a specific deep learning model; they are developed by minimal perturbation of network packet features, and are intended to cause misclassification. Such examples can bypass NIDS or enable the rejection of regular network traffic. Research in the adversarial example detection domain has yielded several prominent methods; however, most of those methods involve computationally expensive retraining steps and require access to labeled data, which are often lacking in IoT network deployments. In this paper, we propose an unsupervised method for detecting adversarial examples that performs early detection based on the intrinsic characteristics of the deep learning model. Our proposed method requires neither computationally expensive retraining nor extra hardware overhead for implementation. For the work in this paper, we first perform adversarial example generation on a deep learning model using autoencoders. After successful adversarial example generation, we perform adversarial example detection using the intrinsic characteristics of the layers in the deep learning model. A robustness analysis of our approach reveals that an attacker can easily bypass the detection mechanism by using low-magnitude log-normal Gaussian noise. Furthermore, we also test the robustness of our detection method against further compromise by the attacker. We tested our approach on the Kitsune datasets, which are state-of-the-art datasets obtained from deployed IoT network scenarios. Our experimental results show an average adversarial example generation time of 0.337 s and an average detection rate of almost 100%. The robustness analysis of our detection method reveals a reduction of almost 100% in adversarial example detection after compromise by the attacker. Full article

The Internet of Vehicles (IoV) presents a vast opportunity for optimised traffic flow, road safety, and enhanced usage experience with the influence of Federated Learning (FL). However, the distributed nature of IoV networks creates certain inherent problems regarding data privacy, security from adversarial attacks, and the handling of available resources. This paper introduces Fed-DTB, a new dynamic trust-based framework for FL that aims to overcome these challenges in the context of IoV. Fed-DTB integrates the adaptive trust evaluation that is capable of quickly identifying and excluding malicious clients to maintain the authenticity of the learning process. A performance comparison with previous approaches is shown, where the Fed-DTB method improves accuracy in the first two training rounds and decreases the per-round training time. The Fed-DTB is robust to non-IID data distributions and outperforms all other state-of-the-art approaches regarding the final accuracy (87–88%), convergence rate, and adversary detection (99.86% accuracy). The key contributions include (1) a multi-factor trust evaluation mechanism with seven contextual factors, (2) correlation-based adaptive weighting that dynamically prioritises trust factors based on vehicular conditions, and (3) an optimisation-based client selection strategy that maximises collaborative reliability. This work opens up opportunities for more accurate, secure, and private collaborative learning in future intelligent transportation systems with the help of federated learning while overcoming the conventional trade-off of security vs. efficiency. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

Sustainable mineral resource management is critical amid escalating environmental concerns and growing demand for minerals in digital and clean energy technologies. While financial technology (FinTech) has been widely recognized for enhancing financial inclusion and economic efficiency, its role in environmental governance—particularly in the mining sector—remains underexplored, especially within developed economies like the United States. This study addresses this gap by examining how FinTech adoption influences mineral sustainability, using time series data from 1998 to 2023. Four FinTech proxies—mobile cellular subscriptions, Internet usage, fixed broadband access, and financial inclusion—were analyzed alongside environmental compliance and investment in sustainable mining technologies. Using the Autoregressive Distributed Lag (ARDL) model and Frequency Domain Causality (FDC) analysis, the results show that greater FinTech adoption significantly reduces mineral depletion rates, indicating improved sustainability. Internet and broadband access exhibit strong long-term impacts, while mobile connectivity and credit access show notable short- and medium-term effects. Investment in sustainable mining technologies further enhances these outcomes. Our findings suggest that FinTech serves as a multidimensional enabler of sustainability through digital inclusion, transparency, and access to green financing. This study provides empirical evidence to guide policymakers in integrating digital financial infrastructure into strategies for sustainable mineral resource governance. Full article