Search Results (156)

Businesses need good defenses against any number of incidents in the continually evolving area of cybersecurity. SIEM (Security Information and Event Management) systems are now important tools for them. The current study offers a comprehensive analysis of SIEM solutions, such as their key features, benefits, installation issues, and suggested procedures. SIEM systems effectively store security event data, giving continuous tracking, interaction, and examination to recognize and deal with threats rapidly. The advantages of this technology include enhanced operating efficiency, streamlined compliance with laws, expedited response to events, and heightened threat detection capabilities. However, the implementation of SIEM systems has many challenges that must be overcome, including intricacies, cognitive exhaustion, data integration complications, and restrictions. To effectively handle these issues, businesses are advised to develop objectives, properly schedule, attend school, and periodically review and enhance their SIEM goals. In addition, organizations may use the complete capabilities of SIEM systems to enhance their cybersecurity stance and mitigate the risks posed by cyberattacks by staying updated with the most recent developments. This study aims to provide a comprehensive examination of Security Information and Event Management (SIEM) systems, with a specific emphasis on important features, benefits, implementation challenges, and suggestions. Full article

Maritime surveillance is critical for ensuring the safety and continuity of sea logistics, port operations, and coastal activities in the presence of anomalies such as unlawful maritime activities, security-related incidents, and anomalous events (e.g., tsunamis or aggressive marine wildlife). Recent advances in unmanned aerial vehicles (UAVs)/drones and computer vision enable automated, wide-area monitoring that can reduce dependence on continuous human observation and mitigate the limitations of traditional methods in complex maritime environments (e.g., waves, ship clutter, and marine animal movement). This study proposes a hybrid anomaly detection and tracking pipeline that integrates YOLOv12, as the primary object detector, with two auxiliary modules: (i) motion assistance for tracking moving anomalies and (ii) stillness (appearance) assistance for tracking slow-moving or stationary anomalies. The system is trained and evaluated on a custom maritime dataset captured using a DJI Mini 2 drone operating around a port area near Bayshore MRT Station (TE29), Singapore. Windsurfers are used as proxy (dummy) anomalies because real anomaly footage is restricted for security reasons. On the held-out test set, the trained model achieves over 90% on Precision, Recall, and mAP50 across all classes. When deployed on real maritime video sequences, the pipeline attains a mean Precision of 92.89% (SD 13.31), a mean Recall of 90.44% (SD 15.24), and a mean Accuracy of 98.50% (SD 2.00%), indicating strong potential for real-world maritime anomaly detection. This proof of concept provides a basis for future deployment and retraining on genuine anomaly footage obtained from relevant authorities to further enhance operational readiness for maritime and coastal security. Full article

Unmanned aerial vehicles (UAVs) increasingly share low-altitude airspace with birds, making early distinguishing between drones and biological targets critical for safety and security. This work addresses long-range scenarios where objects occupy only a few pixels and appearance-based recognition becomes unreliable. We develop a model-driven simulation pipeline that generates synthetic data with a controlled camera model, atmospheric background and realistic motion of three aerial target types: multicopter, fixed-wing UAV and bird. From these sequences, each track is encoded as a time series of image-plane coordinates and apparent size, and a bidirectional long short-term memory (LSTM) network is trained to classify trajectories as drone-like or bird-like. The model learns characteristic differences in smoothness, turning behavior and velocity fluctuations, and to achieve reliable separation between drone and bird motion patterns on synthetic test data. Motion-trajectory cues alone can support early distinguishing of drones from birds when visual details are scarce, providing a complementary signal to conventional image-based detection. The proposed synthetic data and sequence classification pipeline forms a reproducible testbed that can be extended with real trajectories from radar or video tracking systems and used to prototype and benchmark trajectory-based recognizers for integrated surveillance solutions. The proposed method is designed to generalize naturally to real surveillance systems, as it relies on trajectory-level motion patterns rather than appearance-based features that are sensitive to sensor quality, illumination, or weather conditions. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

Ensuring worker safety compliance and secure cargo transportation in complex port environments is critical for modern logistics hubs. However, conventional supervision methods, including manual inspection and passive video monitoring, suffer from limited coverage, poor real-time responsiveness, and low robustness under frequent occlusion, scale variation, and cross-camera transitions, leading to unstable target association and missed risk events. To address these challenges, this paper proposes CSPC-BRS, a real-time multi-object detection and tracking framework for open-channel port scenarios. CSPC (Coordinated Spatial Perception Cascade) enhances the YOLOv8 backbone by integrating CASAM, SPPELAN-DW, and CACC modules to improve feature representation under cluttered backgrounds and degraded visual conditions. Meanwhile, BRS (Bounding Box Reduction Strategy) mitigates scale distortion during tracking, and a Multi-Dimensional Re-identification Scoring (MDRS) mechanism fuses six perceptual features—color, texture, shape, motion, size, and time—to achieve stable cross-camera identity consistency. Experimental results demonstrate that CSPC-BRS outperforms the YOLOv8-n baseline by improving the mAP@0.5:0.95 by 9.6% while achieving a real-time speed of 132.63 FPS. Furthermore, in practical deployment, it reduces the false capture rate by an average of 59.7% compared to the YOLOv8 + Bot-SORT tracker. These results confirm that CSPC-BRS effectively balances detection accuracy and computational efficiency, providing a practical and deployable solution for intelligent safety monitoring in complex industrial logistics environments. Full article

Multi-object tracking (MOT) technology integrates multiple fields such as pattern recognition, machine learning, and object detection, demonstrating broad application potential in scenarios like low-altitude logistics delivery, urban security, autonomous driving, and intelligent navigation. However, in open-world scenarios, existing MOT methods often face challenges of imprecise target category identification and insufficient tracking accuracy, especially when dealing with numerous target types affected by occlusion and deformation. To address this, we propose a multi-object tracking strategy based on multi-cue fusion. This strategy combines appearance features and spatial feature information, employing BYTE and weighted Intersection over Union (IoU) modules to handle target association, thereby improving tracking accuracy. Furthermore, to tackle the challenge of large vocabularies in open-world scenarios, we introduce an open-vocabulary prompting strategy. By incorporating diverse sentence structures, emotional elements, and image quality descriptions, the expressiveness of text descriptions is enhanced. Combined with the CLIP model, this strategy significantly improves the recognition capability for novel category targets without requiring model retraining. Experimental results on the public TAO benchmark show that our method yields consistent TETA improvements over existing open-vocabulary trackers, with gains of 10% and 16% on base and novel categories, respectively. The results demonstrate that the proposed framework offers a more robust solution for open-vocabulary multi-object tracking in complex environments. Full article

The tracking of assets or cargo is one of the main objectives of global logistics and transportation systems, ensuring operational efficiency, security, and timeliness. Currently, battery-operated GPS (Global Positioning System)-based tracking devices are used for this purpose. The main shortcoming of these devices is the lifetime of the batteries because they cannot be replaced or recharged, or because this is simply not economically feasible. Therefore, efficient methods are needed to prolong battery life as much as possible. Various existing energy-saving techniques can be applied to solve this problem. However, none of these consider situations in which multiple tracking devices are transported together and can cooperate to further increase their energy efficiency. In this study, we propose and evaluate the novel lightweight peer-to-peer energy-saving method for nearby wireless battery-powered trackers based on their cooperation. The proposed method is based on the short-range BLE (Bluetooth Low Energy) device discovery mechanism and the dynamic election of the leader tracker (with the highest battery capacity) to report the location of its own and other neighboring trackers to the central server. The experimental evaluation of the proposed method shows that, compared to the traditional approach, where each tracker sends its location individually, the proposed method allows a reduction in the average battery charge required for one position report from 19% to 240% per each cooperating tracker. The average energy consumption for one location report per node decreased from 4.68 mWh using the traditional approach to 3.93 mWh for 2 cooperating devices and 1.92 mWh for 15 cooperating devices. Full article

The permanent magnet synchronous motor (PMSM) is extensively utilized in the power drive systems of Cyber–Physical Systems (CPSs). In scenarios where control signals are subjected to malicious attacks within the network, ensuring that the PMSM achieves its designated speed within a specified timeframe serves as a critical metric for evaluating the efficacy of security control strategies in networked systems. To address practical challenges arising from updates to controlled objects at the physical layer and limitations of control layer algorithms—wherein convergence time for system trajectory tracking errors (TTEors) may extend indefinitely—we have developed a novel resilient control algorithm with predefined-time convergence (PreTC) tailored for uncertain PMSMs susceptible to cyber threats. Firstly, we introduce an innovative Lyapunov stability criterion characterized by an adjustable gain reaching law alongside PreTC. Following this, we design an SMS (SMS) that incorporates PreTC and employ an extreme learning machine (ELM) to facilitate real-time identification of both physical layer models and malicious cyber-attacks. A sliding-mode adaptive resilient controller devoid of explicit physical model information is proposed for CPSs, with Lyapunov stability theory substantiating the system’s predefined-time (PDT) stability. This significantly enhances resilience against malicious cyber-attacks and other uncertainties. Finally, comparative simulations involving four distinct resilient control algorithms demonstrate that our proposed algorithm not only guarantees predetermined convergence times but also exhibits robust resistance to cyber-attacks, parameter perturbations, and external disturbances—notably achieving a motor speed tracking error accuracy of 0.008. These findings validate the superior robustness and effectiveness of our control algorithm against malicious cyber threats. Full article

Automated pollen recognition is a foundational tool for diverse scientific domains, including paleoclimatology, biodiversity monitoring, and agricultural science. However, conventional methods create a critical data bottleneck, limiting the temporal and spatial resolution of ecological analysis. Existing deep learning models often fail to achieve the requisite localization accuracy for microscopic pollen grains, which are characterized by their minute size, indistinct edges, and complex backgrounds. To overcome this, we introduce HieraEdgeNet, a novel object detection framework. The core principle of our architecture is to explicitly extract and hierarchically fuse multi-scale edge information with deep semantic features. This synergistic approach, combined with a computationally efficient large-kernel operator for fine-grained feature refinement, significantly enhances the model’s ability to perceive and precisely delineate object boundaries. On a large-scale dataset comprising 44,471 annotated microscopic images containing 342,706 pollen grains from 120 classes, HieraEdgeNet achieves a mean Average Precision of 0.9501 (mAP@0.5) and 0.8444 (mAP@0.5:0.95), substantially outperforming state-of-the-art models such as YOLOv12n and the Transformer-based RT-DETR family in terms of the accuracy–efficiency trade-off. This work provides a powerful computational tool for generating the high-throughput, high-fidelity data essential for modern ecological research, including tracking phenological shifts, assessing plant biodiversity, and reconstructing paleoenvironments. At the same time, we acknowledge that the current two-dimensional design cannot directly exploit volumetric Z-stack microscopy and that strong domain shifts between training data and real-world deployments may still degrade performance, which we identify as key directions for future work. By also enabling applications in precision agriculture, HieraEdgeNet contributes broadly to advancing ecosystem monitoring and sustainable food security. Full article

This paper presents a comprehensive modeling and control strategy for a pan–tilt (PT) servo system designed for real-time object tracking (specifically face detection) using deep learning and PID control. The system integrates a YOLO-based neural network to detect and localize the target within an image, mapping its coordinates from 3D space onto the 2D image plane through a mathematically defined geometric camera model. A complete mathematical representation of the pan–tilt mechanism is developed, accounting for all relevant forces and system components. Based on this model, a PID controller is designed, and its parameters are identified and implemented using the Ziegler–Nichols tuning method. Experimental results demonstrate that the system effectively tracks objects in real time, exhibiting minimal latency and precise motor responses. These findings suggest that the proposed approach is well-suited for practical applications, including security surveillance, assistive technologies, and interactive robotics. Full article

3D point cloud object tracking plays a critical role in fields such as autonomous driving and robotics, making the security of these models essential. Adversarial attacks are a key approach for studying the robustness and security of tracking models. However, research on the generalization of adversarial attacks for 3D point-cloud-tracking models is limited, and the frequency-domain information of the point cloud’s geometric structure is often overlooked. This frequency information is closely related to the generalization of 3D point-cloud-tracking models. To address these limitations, this paper proposes a novel adversarial method for 3D point cloud object tracking, utilizing frequency-domain attacks based on the importance of frequency bands. The attack operates in the frequency domain, targeting the low-frequency components of the point cloud within the search area. To make the attack more targeted, the paper introduces a frequency band importance saliency map, which reflects the significance of sub-frequency bands for tracking and uses this importance as attack weights to enhance the attack’s effectiveness. The proposed attack method was evaluated on mainstream 3D point-cloud-tracking models, and the adversarial examples generated from white-box attacks were transferred to other black-box tracking models. Experiments show that the proposed attack method reduces both the average success rate and precision of tracking, proving the effectiveness of the proposed adversarial attack. Furthermore, when the white-box adversarial samples were transferred to the black-box model, the tracking metrics also decreased, verifying the transferability of the attack method. Full article

Background/Objectives: Chronic kidney disease (CKD) requires intensive dietary and lifestyle management, yet patient engagement and access to tailored education remain limited, particularly outside clinical settings. This study describes the development and implementation of NephroGo, and evaluates its usability, user engagement, and perceived acceptability among patients with CKD. Methods: The app was developed based on clinical and dietary guidelines, incorporating personalized nutrient recommendations, dialysis tracking, and educational content. Technically, it features a Django backend, Flutter mobile frontend, and secure cloud-based hosting. User feedback was collected through one-time interviews (n = 10) and a standardized Mobile App Rating Scale (MARS) survey (n = 32). Longitudinal usage data over four years were also analyzed. Results: Initially, NephroGo was downloaded by 204 users, of whom 93.6% were considered active users based on defined behavioral engagement thresholds. Over a four-year period, the app accumulated a total of 1670 downloads. This study focuses on evaluating user engagement, usability, and perceived acceptability of the NephroGo app over a four-year period. Most users were female (52.3%) and aged 30–65. Stage 5 CKD patients and those undergoing peritoneal dialysis (PD) had the highest engagement. The most-used feature was the personalized nutrition calculator, with sodium being the most frequently exceeded nutrient. The average MARS score was 4.09 ± 0.66, with functionality rated highest (4.27 ± 0.74). App ratings were significantly higher among users referred by physicians (p = 0.039). Conclusions: NephroGo offers a scalable digital tool to support dietary management and health monitoring, with potential to complement standard nephrology care in a resource-conscious manner. Full article

This paper introduces a novel approach to tree modeling architecture integrated with blockchain technology, aimed at enhancing landscape spatial planning and forest monitoring systems. The primary objective is to develop a low-cost, automated tree CAD modeling methodology combined with blockchain functionalities to support smart forest projects and collaborative design processes. The proposed method utilizes a parametric tree CAD model consisting of four 2D tree-frames with a 45° division angle, enriched with recorded tree-leaves’ texture and color. An “AI Text-by-Voice CAD Programming” technique is employed to create tangible tree-model NFT tokens, forming the basis of a thematic “Internet-of-Trees” blockchain. The main results demonstrate the effectiveness of the blockchain/Merkle hash tree in tracking tree geometry growth and texture changes through parametric transactions, enabling decentralized design, data validation, and planning intelligence. Comparative analysis highlights the advantages in cost, time efficiency, and flexibility over traditional 3D modeling techniques, while providing acceptable accuracy for metaverse projects in smart forests and landscape architecture. Core contributions include the integration of AI-based user voice interaction with blockchain and behavioral data for distributed and collaborative tree modeling, the introduction of a scalable and secure “Merkle hash tree” for smart forest monitoring, and the facilitation of fintech adoption in environmental projects. This framework offers significant potential for advancing metaverse-based landscape architecture, smart forest surveillance, sustainable urban planning, and the improvement of citizen involvement in sustainable forestry paving the way for a greener future. Full article

The widespread application of UAV technology has brought significant security concerns that cannot be ignored, driving considerable attention to anti-unmanned aerial vehicle (UAV) tracking technologies. Anti-UAV tracking faces challenges, including target entry into and exit from the field of view, thermal crossover, and interference from similar objects, where Siamese network trackers exhibit notable limitations in anti-UAV tracking. To address these issues, we propose FSTC-DiMP, an anti-UAV tracking algorithm. To better handle feature extraction in low-Signal-to-Clutter-Ratio (SCR) images and expand receptive fields, we introduce the Large Selective Kernel (LSK) attention mechanism, achieving a balance between local feature focus and global information integration. A spatio-temporal consistency-guided re-detection mechanism is designed to mitigate tracking failures caused by target entry into and exit from the field of view or similar-object interference through spatio-temporal relationship analysis. Additionally, a background augmentation module has been developed to more efficiently utilise initial frame information, effectively capturing the semantic features of both targets and their surrounding environments. Experimental results on the AntiUAV410 and AntiUAV600 datasets demonstrate that FSTC-DiMP achieves significant performance improvements in anti-UAV tracking tasks, validating the algorithm’s strong robustness and adaptability to complex environments. Full article

Robust maritime radar object detection and tracking in maritime clutter environments is critical for maritime safety and security. Conventional Constant False Alarm Rate (CFAR) detectors have limited performance in processing complex-valued radar echoes, especially in complex scenarios where phase information is critical and in the real-time processing of successive echo pulses, while existing deep learning methods usually lack native support for complex-valued data and have inherent shortcomings in real-time compared to conventional methods. To overcome these limitations, we propose a dual-branch sequence feature fusion (DFF) detector designed specifically for complex-valued continuous sea-clutter signals, drawing on commonly used methods in video pattern recognition. The DFF employs dual parallel complex-valued U-Net branches to extract multilevel spatiotemporal features from distance profiles and Doppler features from distance–Doppler spectrograms, preserving the critical phase–amplitude relationship. Subsequently, the sequential feature-extraction module (SFEM) captures the temporal dependence in both feature streams. Next, the Adaptive Weight Learning (AWL) module dynamically fuses these multimodal features by learning modality-specific weights. Finally, the detection module generates the object localisation output. Extensive evaluations on the IPIX and SDRDSP datasets show that DFF performs well. On SDRDSP, DFF achieves 98.76% accuracy and 68.75% in F1 score, which significantly outperforms traditional CFAR methods and state-of-the-art deep learning models in terms of detection accuracy and false alarm rate (FAR). These results validate the effectiveness of DFF for reliable maritime object detection in complex clutter environments through multimodal feature fusion and sequence-dependent modelling. Full article