Search Results (34)

Securing Industrial IoT (IIoT) network environments remains a significant challenge due to the increasing complexity of interconnected sensors, actuators, gateways, and control systems, which are frequent targets of cyberattacks. These threats can lead to operational disruptions, financial losses, and safety risks. This paper proposes an efficient multi-stage intrusion detection framework based on an enhanced Extreme Gradient Boosting (XGBoost) model for IIoT environments. The proposed framework integrates data preprocessing, class imbalance handling, hyperparameter optimization, probability calibration, and class-specific decision thresholds within a unified pipeline. In addition, calibrated probability outputs are utilized as continuous indicators of prediction confidence, enabling more reliable and risk-aware decision-making. The hierarchical multi-stage design decomposes the detection task into progressively refined classification levels, improving discrimination among complex and overlapping attack categories. The framework is evaluated using the Edge-IIoTset benchmark dataset, which reflects realistic IIoT network traffic under both normal and malicious conditions. Experimental results demonstrate that the proposed approach achieved significant performance improvements, including up to 21% increase in recall and 15% improvement in macro F1 score compared to the baseline models. Furthermore, the model exhibits low inference latency and supports efficient deployment in time-sensitive IIoT monitoring scenarios. These results indicate that the proposed framework provides an effective and scalable solution for multi-class cyber threat detection in IIoT networks. Full article

Embedded Field-Programmable Gate Array (FPGA)-Advanced RISC Machine (ARM) systems used in industrial and Internet of Things (IoT) environments increasingly operate as network-connected edge devices. While such connectivity enables distributed processing and remote monitoring, it also exposes embedded vision nodes to security threats, including command injection, frame replay, data tampering, and abnormal communication traffic. This paper presents a hardware-assisted security architecture for an FPGA-ARM embedded vision system designed for high-speed image acquisition and network streaming. The proposed solution integrates several lightweight protection mechanisms directly into the FPGA processing pipeline, including frame replay detection, cyclic redundancy check (CRC)-based frame integrity verification, frame sequence monitoring, authenticated command execution, communication anomaly monitoring, and hardware-rooted trust primitives, such as a ring-oscillator physical unclonable function (PUF) and a pseudo-random generator. Optional secure communication is provided via a lightweight ASCON-authenticated encryption core. The architecture was implemented on a Cyclone V System-on-Chip (SoC) platform using an industrial Camera Link camera and evaluated in a low-latency image-acquisition setup operating at 100 fps, with data throughput exceeding 1 Gbps. Experimental results demonstrate that the proposed security architecture introduces only about 1.6% additional FPGA logic utilization while maintaining full real-time acquisition performance. The presented approach demonstrates that practical hardware-level security mechanisms can be integrated into FPGA-based embedded vision nodes with minimal architectural modifications and negligible performance overhead. Full article

Air pollution caused by fine particulate matter (PM_2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM_2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM_2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO₂, SO₂), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R² = 0.77 (RMSE = 24.75 µg m⁻³), XGBoost R² = 0.76 (RMSE = 26.32 µg m⁻³), CatBoost R² = 0.73 (RMSE = 30.39 µg m⁻³), and LightGBM R² = 0.70 (RMSE = 32.75 µg m⁻³). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R² = 0.77; RMSE = 23.37 µg m⁻³). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO₂ were the most influential predictors of PM_2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article

Smart-city IoT ecosystems depend on a large number of devices with limited resources, which often lack built-in security mechanisms. While traditional cloud-based or gateway-centric intrusion detection systems (IDSs) offer essential security, they are still characterized by high detection latency, considerable bandwidth demand, and a lack of precise monitoring of single device actions. This study proposes the Edge AI Bridge, a novel micro-computing security layer positioned between IoT devices and the gateway to enable early-stage threat interception. The architecture integrates embedded AI hardware with a hybrid pipeline, utilizing unsupervised anomaly detection for behavioral profiling and a lightweight signature-matching module to minimize false positives. System operations—including localized traffic inspection, protocol parsing, and feature extraction—are performed before data aggregation, which preserves device-level privacy and reduces the computational burden on the IoT gateway. The contemporary CIC-IoT-2023 dataset, which captures a wide range of smart-city protocols and attack vectors, is used to evaluate the architecture. The Edge AI Bridge leads to a significant reduction in detection latency—≈50 ms on average as opposed to the 500 ms of cloud-based solutions—while the resource footprint is kept low to about 20% CPU utilization. The Edge AI Bridge demonstrates a potential solution that is scalable, modular, and can preserve privacy while improving the cyber resilience of the smart-city infrastructures that are large, heterogeneous, and difficult to manage. Full article

Monitoring oil pipelines is crucial for effective infrastructure management and maintenance, as it helps prevent threats such as vandalism and leaks that can lead to catastrophic events. Pipeline leaks pose significant environmental and economic risks; however, existing detection methods are often expensive, slow, or unreliable, limiting their effectiveness for real-time applications. This study proposes a lightweight thermal-imaging-based intelligent leak detection system that integrates Convolutional Neural Networks (CNN), Autoencoder (AE), and Knowledge Distillation (KD), suitable for deployment on edge devices. The proposed system addresses challenges associated with existing pipeline detection techniques, including large model sizes, high transmission latency, and excessive energy consumption. Thermal images of pipelines are captured and compressed using an autoencoder before being processed by a CNN model optimized through knowledge distillation. The model was trained and tested on a locally collected thermal image dataset and designed for deployment on edge devices such as Raspberry Pi to simulate edge computing scenarios. Experimental results demonstrate that the proposed CNN + KD + AE model achieved 98% accuracy, 98% precision, 98% recall, and an F1-score of 98%, outperforming baseline models such as MobileNetV2 (91%), InceptionV3 (84%), EfficientNet-Lite (81%), and ResNet (74%). Furthermore, the number of trainable parameters was significantly reduced to 1.18 million, with a compact model size of 4.51 MB. These findings confirm the system’s suitability for real-time leak detection in remote and resource-constrained environments, contributing to the development of cost-effective, scalable, and energy-efficient solutions for intelligent pipeline monitoring. Full article

The rapid growth of Internet of Medical Things (IoMT) devices has revolutionized diagnostics and patient care within smart healthcare networks. However, this progress has also expanded the attack surface due to the heterogeneity and interconnectivity of medical devices. To overcome the limitations of traditional batch-trained security models, this study proposes an adaptive online intrusion detection framework designed for real-time operation in dynamic healthcare environments. The system combines Leveraging Bagging with Hoeffding Tree classifiers for incremental learning while integrating the Page–Hinkley test to detect and adapt to concept drift in evolving attack patterns. A modular and scalable network architecture supports centralized monitoring and ensures seamless interoperability across various IoMT protocols. Implemented within a low-latency, high-throughput stream-processing pipeline, the framework meets the stringent clinical requirements for responsiveness and reliability. To simulate streaming conditions, we evaluated the model using the CICIoMT2024 dataset, presenting one instance at a time in random order to reflect dynamic, real-time traffic in IoMT networks. Experimental results demonstrate exceptional performance, achieving accuracies of 0.9963 for binary classification, 0.9949 for six-class detection, and 0.9860 for nineteen-class categorization. These results underscore the framework’s practical efficacy in protecting modern healthcare infrastructures from evolving cyber threats. Full article

Oil spills represent a severe threat to aquatic ecosystems, requiring rapid and reliable detection methods to support environmental response. Hyperspectral imaging (HSI) offers high spectral resolution for distinguishing hydrocarbon types, but its effective use depends on the performance and robustness of deep learning (DL) models, especially under data-limited conditions. This study presents a systematic evaluation of convolutional neural network (CNN) configurations for oil spill classification in visible-near-infrared (VNIR) hyperspectral data, examining the influence of architectural depth and hyperparameters such as the number of convolutional kernels, neuron density, and dropout rate. Two architectures were tested across 54 configurations and two training set sizes (259 and 518 samples). Results show that a compact architecture with an additional max pooling layer achieved near-perfect accuracy (>0.99) with reduced complexity and greater robustness, outperforming its deeper counterpart. Importantly, this study reveals that under small-sample scenarios, optimal performance can still be achieved by carefully balancing model capacity, favouring moderate convolutional depth and high neuron density, while avoiding over-regularisation. These findings provide practical guidance for designing efficient CNNs for UAV-based oil spill monitoring and lay the groundwork for future integration into local real-time processing pipelines and transfer learning applications. Full article

Fractures are a leading cause of morbidity and mortality in Thoroughbred racehorses, posing a significant threat to their welfare and careers. This study introduces a deep learning model specifically designed to facilitate fracture detection in equine athletes. By leveraging extensive training on human fracture data and refining the model with equine imaging, it highlights the transformative potential of transfer learning across species and medical contexts. This approach is not limited to equine fractures but could be adapted for use in detecting injuries or conditions in other veterinary species and even human healthcare applications. A comprehensive databank of radiographs, sourced from public archives and equine hospitals, was curated to encompass diverse conditions (fracture and non-fracture), ensuring robust pattern recognition. The architecture integrates a Vision Transformer for global context modelling with a ResNet backbone and loss function to optimize local feature extraction and cross-species adaptability. The pipeline achieved 96.7% accuracy for modality classification, 97.2% accuracy for projection recognition, and fracture localization intersection over union values of 0.71–0.84 across equine datasets. This work bridges advancements in human and veterinary medicine, opening pathways for AI-driven solutions that extend beyond fractures, fostering improved diagnostic precision and broader applications across species (felines, canines, etc.). By integrating advanced imaging techniques with AI, this study aims to set a foundation for more comprehensive and versatile health monitoring systems. Full article

Cotton spider mites pose a significant threat to cotton production, while traditional manual investigation and blanket pesticide application are inefficient for precision pest management in large-scale cotton fields. To address this challenge, this study developed an integrated UAV multispectral remote sensing system for spider mite monitoring and precision spraying. Multispectral imagery was acquired from cotton fields in Shaya County, Xinjiang using UAV-mounted cameras, and vegetation indices including RDVI, MSAVI, SAVI, and OSAVI were selected through feature optimization. Comparative evaluation of three machine learning models (Logistic Regression, Random Forest, and Support Vector Machine) and two deep learning models (1D-CNN and MobileNetV2) was conducted. Considering classification performance and computational efficiency for real-time UAV deployment, Random Forest was identified as optimal, achieving 85.47% accuracy, an 85.24% F1-score, and an AUC of 0.912. The model generated centimeter-level spatial distribution maps for precise spray zone delineation. An improved NSGA-III multi-objective path optimization algorithm was proposed, incorporating PCA-based heuristic initialization, differential evolution operators, and co-evolutionary dual population strategies to optimize deadheading distance, energy consumption, operation time, turning frequency, and load balancing. Ablation study validated the effectiveness of each component, with the fully improved algorithm reducing IGD by 59.94% and increasing HV by 5.90% compared to standard NSGA-III. Field validation showed 98.5% coverage of infested areas with only 3.6% path repetition, effectively minimizing pesticide waste and phytotoxicity risks. This study established a complete technical pipeline from monitoring to application, providing a valuable reference for precision pest control in large-scale cotton production systems. The framework demonstrated robust performance across multiple field sites, though its generalization is currently limited to one geographic region and growth stage. Future work will extend its application to additional cotton varieties, growth stages, and geographic regions. Full article

Seagrass meadows are essential coastal ecosystems that provide key ecological services, including carbon sequestration, sediment stabilization, and shoreline protection. Increasing threats from natural and anthropogenic stressors highlight the need for efficient, reproducible, and non-invasive monitoring solutions. This study evaluates the performance of low-cost commercial drones for seagrass assessment in shallow coastal waters, with an emphasis on freely accessible mission-planning and photogrammetric workflows. Field surveys were conducted along the Calabrian coast (southern Italy), where automated flight paths were generated using the software WaypointMap, and high-resolution orthophotos were generated using the WebODM software and subsequently analyzed in QGIS for seagrass patch detection, mapping, and surface estimation. The methodological pipeline is described in detail to facilitate full reproducibility. Compared with traditional diver-based methods, this workflow offers faster data collection, broader spatial coverage, and minimal environmental disturbance. Although some limitations remain, the results demonstrate that combining low-cost drones with open-source tools provides a practical and scalable solution for routine monitoring. This approach has strong potential for integration into routine coastal habitat assessment, supports early impact detection, and contributes to evidence-based conservation and management strategies. Full article

Cybersecurity teams rely on signature-based scanners such as Loki, a command-line tool for scanning malware, to identify Indicators of Compromise (IOCs), malicious artifacts, and YARA-rule matches. However, the raw Loki log output delivered as CSV or plaintext is challenging to interpret without additional visualization and correlation tools. Therefore, this research discusses the creation of a web-based dashboard that displays results from the Loki scanner. The project focuses on processing and displaying information collected from Loki’s scans, which are available in log files or CSV format. DIGITRACKER was developed as a proof-of-concept (PoC) to process this data and present it in a user-friendly, visually appealing way, enabling system administrators and cybersecurity teams to monitor potential threats and vulnerabilities effectively. By leveraging modern web technologies and dynamic data visualization, the tool enhances the user experience, transforming raw scan results into a well-organized, interactive dashboard. This approach simplifies the often-complicated task of manual log analysis, making it easier to interpret output data and to support low-budget or resource-constrained cybersecurity teams by transforming raw logs into actionable insights. The project demonstrates the dashboard’s effectiveness in identifying and addressing threats, providing valuable tools for cybersecurity system administrators. Moreover, our evaluation shows that DIGITRACKER can process scan logs containing hundreds of IOC alerts within seconds and supports multiple concurrent users with minimal latency overhead. In test scenarios, the integrated Loki scans were achieved, and the end-to-end pipeline from the end of the scan to the initiation of dashboard visualization incurred an average latency of under 20 s. These results demonstrate improved threat visibility, support structured triage workflows, and enhance analysts’ task management. Overall, the system provides a practical, extensible PoC that bridges the gap between command-line scanners and operational security dashboards, with new scan results displayed on the dashboard faster than manual log analysis. By streamlining analysis and enabling near-real-time monitoring, the PoC tool DIGITRACKER empowers cyber defense initiatives and enhances overall system security. Full article

Plastic pollution poses severe threats to ecosystems, human health, and economies as plastics fragment into macro- and microplastics that accumulate across marine and terrestrial environments. Conventional monitoring is constrained by scale, cost, and resources, particularly in under-resourced regions, whereas citizen science provides an inclusive, community-driven alternative for data collection, analysis, and remediation to support evidence-based policy. This systematic review advances the field through three novel contributions: a refined participatory typology that explicitly prioritizes co-creative models for equitable engagement in the Global South; the first comprehensive synthesis of direct citizen involvement in plastic bioremediation, including community microbial isolation, household biodegradation trials, and real-world testing of biodegradable materials; and a new conceptual framework positioning citizen science as the central nexus linking upstream prevention, technological innovation, bioremediation, and global governance. Findings highlight large-scale geotagged datasets, behavioral change, and policy influence, while persistent challenges include data standardization, digital exclusion, and Global North bias. We therefore advocate institutional mainstreaming through dedicated policy offices, decolonial integration of indigenous knowledge, and hybrid citizen–lab validation pipelines, especially in underrepresented regions such as Africa, establishing citizen science as a transformative mechanism for participatory and equitable responses to escalating plastic pollution. Full article

The digital transformation of electric power systems into smart grids has significantly expanded the cybersecurity risk landscape of the energy sector. While advanced sensing, communication, automation, and data-driven control improve efficiency, flexibility, and renewable energy integration, they also introduce complex cyber–physical interdependencies and new vulnerabilities across interconnected technical and organisational domains. This study adopts a scoping review methodology in accordance with PRISMA-ScR to systematically analyse smart grid cybersecurity from an architecture-aware and resilience-oriented perspective. Peer-reviewed scientific literature and authoritative institutional sources are synthesised to examine modern smart grid architectures, key security challenges, major cyberthreats, and documented real-world cyber incidents affecting energy infrastructure up to 2025. The review systematically links architectural characteristics such as field devices, communication networks, software platforms, data pipelines, and externally operated services to specific threat mechanisms and observed attack patterns, illustrating how cyber risk propagates across interconnected grid components. The findings show that cybersecurity challenges in smart grids arise not only from technical vulnerabilities but also from architectural dependencies, software supply chains, operational constraints, and cross-sector coupling. Based on the analysis of historical incidents and emerging research, the study identifies key defensive strategies, including zero-trust architectures, advanced monitoring and anomaly detection, secure software lifecycle management, digital twins for cyber–physical testing, and cyber-resilient grid design. The review concludes that cybersecurity in smart grids should be treated as a systemic and persistent condition, requiring resilience-oriented approaches that prioritise detection, containment, recovery, and safe operation under adverse conditions. Full article

The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system targeting two high-priority IoV security pillars: availability (traffic overload) and integrity/authenticity (spoofing), with spoofing evaluated across multiple subclasses (GAS, RPM, SPEED, and steering wheel). In the offline phase, deep learning and hybrid models were benchmarked on the vehicular CAN bus dataset CICIoV2024, with the BiLSTM-XGBoost hybrid chosen for its balance between accuracy and inference speed. Real-time deployment uses a TinyML-distilled CNN on vehicles for ultra-lightweight, low-latency detection, while RSU-level BiLSTM-XGBoost performs a deeper temporal analysis. A Kafka–Spark Streaming pipeline supports localized classification, prevention, and dashboard-based monitoring. In baseline, stealth, and coordinated modes, the evaluation achieved accuracy, precision, recall, and F1-scores all above 97%. The mean end-to-end inference latency was 148.67 ms, and the resource usage was stable. The framework remains robust in both high-traffic and low-frequency attack scenarios, enhancing operator situational awareness through real-time visualizations. These results demonstrate a scalable, explainable, and operator-focused IDPS well suited for securing SC-IoV deployments against evolving threats. Full article

Hulun Lake, a UNESCO Biosphere Reserve, faces mounting threats from extreme climate events and anthropogenic pressures, highlighting the need for robust biodiversity monitoring. Environmental DNA (eDNA) has emerged as a promising tool for aquatic biomonitoring, yet different bioinformatic pipelines—such as Amplicon Sequence Variant (ASV) and Operational Taxonomic Unit (OTU) clustering—may yield divergent results. This study compares ASV and OTU clustering approaches in eDNA metabarcoding alongside traditional capture-based surveys to assess fish diversity in Hulun Lake. Across all methods, we identified 43 taxa (40 species), including the critically endangered Acheilognathus hypselonotus and vulnerable Choi’s spiny loach (Cobitis choii). While eDNA methods detected 2~3 times more species than in nets (13 species), strong methodological correlations (p < 0.001) were observed between net frequencies and eDNA-derived relative abundances (based on both ASV and OTU datasets using 4th-root and log transformations). Clustering analysis of capture-based data revealed four distinct ecological zones: the areas near tourist facilities, Wuerxun River inflow region, Wulan Nuoer Lake (connected via the Wuerxun River), and the Lake Centre. Significant spatial variation (p < 0.05) between these four zones was found in eDNA datasets, whereas nets captured more heterogeneous patterns, consistent with previous studies. Community structures were shaped by both generalists (e.g., Cyprinus carpio, Hemiculter bleekeri) and habitat specialists such as Amur catfish (Silurus asotus). The Lake Centre hosted a unique assemblage, likely due to reduced human disturbance. Overall, both eDNA clustering methods outperformed capture-based survey in detecting species richness and offered semi-quantitative insights. However, discrepancies between ASV and OTU approaches were evident in resolving fine-scale community differences. We recommend an integrated monitoring strategy that combines the sensitivity of eDNA with the abundance resolution of net captured to inform spatially targeted conservation and habitat protection in this vulnerable ecosystem. Full article