Search Results (216)

The offshore implementation of vertical-axis wind turbines (VAWTs) presents a promising new paradigm for advancing marine wind energy utilization, owing to their omnidirectional wind acceptance, compact structural design, and potential for lower maintenance costs. However, VAWTs still face major aerodynamic challenges, particularly due to the pitching motion, where the angle of attack varies cyclically with the blade azimuth. This leads to strong unsteady effects and susceptibility to dynamic stalls, which significantly degrade aerodynamic performance. To address these unresolved issues, this study conducts a comprehensive investigation into the dynamic stall behavior and wake vortex evolution induced by Darrieus-type pitching motion (DPM). Quasi-three-dimensional CFD simulations are performed to explore how variations in blade geometry influence aerodynamic responses under unsteady DPM conditions. To efficiently analyze geometric sensitivity, a surrogate model based on a radial basis function neural network is constructed, enabling fast aerodynamic predictions. Sensitivity analysis identifies the curvature near the maximum thickness and the deflection angle of the trailing edge as the most influential geometric parameters affecting lift and stall behavior, while the blade thickness is shown to strongly impact the moment coefficient. These insights emphasize the pivotal role of blade shape optimization in enhancing aerodynamic performance under inherently unsteady VAWT operating conditions. Full article

In the era of digital communication, securing sensitive information against increasingly sophisticated cyber threats remains a critical challenge. Chaos synchronization, while promising for secure communication and control systems, faces key limitations, including high sensitivity to parameter mismatches and initial conditions, vulnerability to noise, and difficulties in maintaining stability for high-dimensional systems. This paper presents a novel secure communication system based on multi-level synchronization of three distinct chaotic systems: the Bhalekar–Gejji (BG) system, the Chen system, and a 3D chaotic oscillator. Utilizing the nonlinear active control (NAC) method, we achieve robust synchronization between master and slave systems, ensuring the stability of the error dynamics through Lyapunov theory. The proposed triple-cascade masking technique enhances security by sequentially embedding the information signal within the chaotic outputs of these systems, making decryption highly challenging without knowledge of all three systems. Numerical simulations and Simulink implementations validate the effectiveness of the synchronization and the secure communication scheme. The results demonstrate that the transmitted signal becomes unpredictable and resistant to attacks, significantly improving the security of chaos-based communication. This approach offers a promising framework for applications requiring high levels of data protection, with potential extensions to image encryption and other multimedia security domains. Full article

Face recognition has achieved tremendous success in both its theory and technology. However, with increasingly realistic attacks, such as print photos, replay videos, and 3D masks, as well as new attack methods like AI-generated faces or videos, face recognition systems are confronted with significant challenges and risks. Distinguishing between real and fake faces, i.e., face anti-spoofing (FAS), is crucial to the security of face recognition systems. With the advent of large-scale academic datasets in recent years, FAS based on deep learning has achieved a remarkable level of performance and now dominates the field. This paper systematically reviews the latest advancements in FAS based on deep learning. First, it provides an overview of the background, basic concepts, and types of FAS attacks. Then, it categorizes existing FAS methods from the perspectives of RGB (red, green and blue) modality and other modalities, discussing the main concepts, the types of attacks that can be detected, their advantages and disadvantages, and so on. Next, it introduces popular datasets used in FAS research and highlights their characteristics. Finally, it summarizes the current research challenges and future directions for FAS, such as its limited generalization for unknown attacks, the insufficient multi-modal research, the spatiotemporal efficiency of algorithms, and unified detection for presentation attacks and deepfakes. We aim to provide a comprehensive reference in this field and to inspire progress within the FAS community, guiding researchers toward promising directions for future work. Full article

We propose a deep learning-based face anti-spoofing method that utilizes both RGB and depth images for face recognition. The proposed method can detect spoofing attacks across various domain types using domain adversarial learning for preventing overfitting to a specific domain. A pre-trained face detection model and a face segmentation model are employed to detect a facial region from RGB images. The pixels outside the facial region in the corresponding depth image are replaced with the depth values of the nearest pixels in the facial region to minimize background influence. Subsequently, a network comprising convolutional layers and a self-attention layer extracts features from RGB and depth images separately, then fuses them to detect spoofing attacks. The proposed network is trained using domain adversarial learning to ensure robustness against various types of face spoofing attacks. The experiment results show that the proposed network reduces the average Attack Presentation Classification Error Rate (APCER) to 11.12% and 8.00% compared to ResNet and MobileNet, respectively. Full article

Federated Learning (FL) presents a promising approach for collaborative intrusion detection while preserving data privacy. However, current FL frameworks face challenges with non-independent and identically distributed (non-IID) data and class imbalances in network security contexts. This paper introduces Dynamic Sampling-FedIDS (DS-FedIDS), a novel framework that enhances federated intrusion detection through adaptive sampling and personalization. DS-FedIDS extends the Federated Learning with Personalization Layers (FedPer) architecture by incorporating dynamic up/down sampling to address class imbalance issues in network security datasets. The framework maintains a global model for shared attack detection while enabling client-specific adaptation through personalized layers. Our approach effectively handles heterogeneous network environments, including Content Delivery Networks, Internet of Things, and industrial systems, each with distinct traffic patterns and attack profiles. Experimental results demonstrate that DS-FedIDS outperforms baseline FedPer in accuracy and efficiency, achieving superior detection rates across diverse attack classes while maintaining reasonable training overhead. Notably, DS-FedIDS excels in detecting minority attack classes and adapting to client-specific normal traffic patterns, making it ideal for real-world intrusion detection scenarios with inherently imbalanced and heterogeneous data distributions. Full article

A road network is an important spatial carrier for the efficient and reliable operation of urban services and material flows. In recent years, the “high road density, small block size” trend has become a major focus in urban planning practices. However, whether high-density road networks are highly resilient lacks quantitative evidence. This study presents a multi-scale analytical framework for measuring road network resilience from a topological perspective. We abstract 186 ideal orthogonal grid density models from an actual urban road network, quantifying resilience under two disturbance scenarios: random failures and intentional attacks. The results indicate that road network density indeed has a significant impact on resilience, with both scenarios showing a trend where higher densities correlate with greater resilience. However, the increase in resilience value under the intentional attack scenario is significantly higher than that under the random failure scenario. The findings indicate that network density plays a decisive role in determining resilience levels when critical edges fail. This is attributed to the greater presence of loops in denser networks, which helps maintain connectivity even under intentional disruption. In the random failure scenario, network resilience depends on the combined effects of the node degree and density. This study offers quantitative insights into the design of resilient urban forms in the face of disruptive events, establishing reference benchmarks for road network spacing at both meso- and micro-scales. The results provide practical guidance for resilient city planning in both newly developed and existing urban areas, supporting informed decision-making in urban morphology and disaster risk management. Full article

Forests play a vital role in maintaining ecological balance, making accurate forest monitoring technologies essential. Remote sensing point cloud data always capture distinctive geometric features of forests, including the cylindrical symmetry of tree trunks and the radial symmetry of canopies. However, the inherent complexity of point cloud data, combined with their vulnerabilities to adversarial attacks, often disrupts these symmetrical patterns, significantly limiting the practical application of deep learning models in forest monitoring. This research presents a novel approach to enhance the robustness of individual tree segmentation networks by combining data augmentation and adversarial training techniques. Our method employs the FGSM algorithm and Gaussian noise attack to generate adversarial samples while utilizing data denoising and controlled noise injection for data augmentation. A dynamic adversarial training framework can adaptively adjust the proportion of adversarial samples during the network training stage to optimize the model. Using remote sensing point cloud datasets from Wisconsin, the experimental results demonstrate the effectiveness of the individual tree segmentation networks, PointNet++ and DBSCAN, in reducing attack success rates whilst improving the stability and accuracy of segmentation results under various adversarial conditions. This study highlights the potential for more robust forest monitoring systems capable of maintaining accuracy even when faced with data perturbations or intentional interference. Full article

With advancements in hardware, high-quality head-mounted display (HMD) devices are being developed by numerous companies, driving increased consumer interest in AR, VR, and MR applications. This proliferation of HMD devices opens up possibilities for a wide range of applications beyond entertainment. Most commercially available HMD devices are equipped with internal inward-facing cameras to record the periocular areas. Given the nature of these devices and captured data, many applications such as biometric authentication and gaze analysis become feasible. To effectively explore the potential of HMDs for these diverse use-cases and to enhance the corresponding techniques, it is essential to have an HMD dataset that captures realistic scenarios. In this work, we present a new dataset of periocular videos acquired using a virtual reality headset called VRBiom. The VRBiom, targeted at biometric applications, consists of 900 short videos acquired from 25 individuals recorded in the NIR spectrum. These 10 s long videos have been captured using the internal tracking cameras of Meta Quest Pro at 72 FPS. To encompass real-world variations, the dataset includes recordings under three gaze conditions: steady, moving, and partially closed eyes. We have also ensured an equal split of recordings without and with glasses to facilitate the analysis of eye-wear. These videos, characterized by non-frontal views of the eye and relatively low spatial resolutions ($400\times 400$), can be instrumental in advancing state-of-the-art research across various biometric applications. The VRBiom dataset can be utilized to evaluate, train, or adapt models for biometric use-cases such as iris and/or periocular recognition and associated sub-tasks such as detection and semantic segmentation. In addition to data from real individuals, we have included around 1100 presentation attacks constructed from 92 PA instruments. These PAIs fall into six categories constructed through combinations of print attacks (real and synthetic identities), fake 3D eyeballs, plastic eyes, and various types of masks and mannequins. These PA videos, combined with genuine (bona fide) data, can be utilized to address concerns related to spoofing, which is a significant threat if these devices are to be used for authentication. The VRBiom dataset is publicly available for research purposes related to biometric applications only. Full article

Threshold Multi-Party Private Set Intersection (TMP-PSI) is a cryptographic protocol that enables an element from the receiver’s set to be included in the intersection result if it appears in the sets of at least $t-1$ other participants, where t represents the threshold. This protocol is crucial for a variety of applications, such as anonymous electronic voting, online ride-sharing, and close-contact tracing programs. However, most existing TMP-PSI schemes are designed based on threshold homomorphic encryption, which faces significant challenges, including low computational efficiency and a high number of communication rounds. To overcome these limitations, this study introduces the Threshold Oblivious Pseudo-Random Function (tOPRF) to fulfill the requirements of threshold encryption and decryption. Additionally, we extend the concept of the Oblivious Programmable Pseudo-Random Function (OPPRF) to develop a novel cryptographic primitive termed the Partially OPPRF (P-OPPRF). This new primitive retains the critical properties of obliviousness and randomness, along with the security assurances inherited from the OPPRF, while also offering strong resistance against malicious adversaries. Leveraging this primitive, we propose the first malicious-secure TMP-PSI protocol, named QMP-PSI, specifically designed for applications like anonymous electronic voting systems. The protocol effectively counters collusion attacks among multiple parties, ensuring robust security in multi-party environments. To further enhance voting efficiency, this work presents a cloud-assisted QMP-PSI to outsource the computationally intensive phases. This ensures that the computational overhead for participants is solely dependent on the set size and statistical security parameters, thereby maintaining security while significantly reducing the computational burden on voting participants. Finally, this work validates the protocol’s performance through extensive experiments under various set sizes, participant numbers, and threshold values. The results demonstrate that the protocol surpasses existing schemes, achieving state-of-the-art (SOTA) performance in communication overhead. Notably, in small-scale voting scenarios, it exhibits exceptional performance, particularly when the threshold is small or close to the number of participants. Full article

The aerial–terrestrial integrated Internet of Things (ATI-IoT) utilizes both aerial platforms (e.g., drones and high-altitude platform stations) and terrestrial networks to establish comprehensive and seamless connectivity across diverse geographical regions. The integration offers significant advantages, including expanded coverage in remote and underserved areas, enhanced reliability of data transmission, and support for various applications such as emergency communications, vehicular ad hoc networks, and intelligent agriculture. However, due to the inherent openness of wireless channels, ATI-IoT faces potential network threats and attacks, and its security issues cannot be ignored. In this regard, incorporating physical layer security techniques into ATI-IoT is essential to ensure data integrity and confidentiality. Motivated by the aforementioned factors, this review presents the latest advancements in ATI-IoT that facilitate physical layer security. Specifically, we elucidate the endogenous safety and security of wireless communications, upon which we illustrate the current status of aerial–terrestrial integrated architectures along with the functions of their components. Subsequently, various emerging techniques (e.g., intelligent reflective surfaces-assisted networks, device-to-device communications, covert communications, and cooperative transmissions) for ATI-IoT enabling physical layer security are demonstrated and categorized based on their technical principles. Furthermore, given that aerial platforms offer flexible deployment and high re-positioning capabilities, comprehensive discussions on practical applications of ATI-IoT are provided. Finally, several significant unresolved issues pertaining to technical challenges as well as security and sustainability concerns in ATI-IoT enabling physical layer security are outlined. Full article

Zr-4 alloy tubes, as the primary cladding material in nuclear reactor cores, face the critical challenge of oxidative attack in 1200 °C steam environments. To address this issue, high-temperature oxidation-resistant coatings fabricated via extreme high-speed laser cladding (EHLA) present a promising mitigation strategy. In this study, Y₂O₃-modified (0.0–5.0 wt.%) Cr/FeCrAl composite coatings were designed and fabricated on Zr-4 substrates using the EHLA process, followed by systematic investigation of Y doping effects on coating microstructures and steam oxidation resistance (1200 °C, H₂O atmosphere). Experimental results demonstrate that Y₂O₃ doping remarkably enhanced the oxidation resistance, with optimal performance achieved at 2.0 wt.% Y₂O₃ (31% oxidation mass gain compared to the substrate after 120-min exposure). Microstructural analysis reveals that the dense grain boundary network facilitates rapid surface diffusion of Al, promoting continuous Al₂O₃ protective film formation. Additionally, Y segregation at grain boundaries suppressed outward diffusion of Cr³⁺ cations, effectively inhibiting void formation at the oxide-coating interface and improving interfacial stability. The developed rare-earth-oxide-doped composite coating via extreme high-speed laser cladding process shows promising applications in surface-strengthening engineering for nuclear reactor Zr-4 alloy cladding tubes, providing both theoretical insights and technical references for the design of high-temperature oxidation-resistant coatings in nuclear industry. Full article

Federated learning offers a powerful approach for training models across decentralized datasets, enabling the creation of machine learning models that respect data privacy. However, federated learning faces significant challenges due to its vulnerability to adversarial attacks, especially when clients have diverse and potentially malicious data distributions. These challenges can lead to severe degradation in the global model’s performance and generalization. In this paper, we present a novel federated image adversarial training framework that combines client-specific adversarial example generation with a robust trimmed aggregation technique. By creating adversarial examples tailored to each client’s local data, our method strengthens individual model defenses against adversarial attacks. Meanwhile, the trimmed aggregation strategy ensures the global model’s robustness by mitigating the impact of harmful or low-quality updates during the model aggregation process. This framework effectively addresses both the issue of data heterogeneity and adversarial threats in federated learning settings. Our experimental results on standard image classification datasets show that the proposed approach significantly enhances model robustness, surpassing existing methods in defending against various adversarial attacks while maintaining high classification accuracy. This framework holds strong promise for real-world applications, particularly in privacy-sensitive domains where both security and model reliability are essential. Full article

In the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously integrating a teacher–student learning framework with cutting-edge PAD methodologies. Our approach is anchored in the realization that conventional PAD models, while effective to a degree, falter in the face of novel, unseen attack vectors and complex variations. As a solution, we suggest a novel architecture where a teacher network, trained on a comprehensive dataset embodying a broad spectrum of attacks and genuine instances, distills knowledge to a student network. The student network, specifically focusing on the nuanced detection of genuine samples in target domains, leverages minimalist yet representative attack data. This methodology is enriched by incorporating facial expressions, dynamic backgrounds, and adversarially generated attack simulations, aiming to mimic the sophisticated techniques attackers might employ. Through rigorous experimentation and validation on benchmark datasets, our results manifested a substantial leap in classification accuracy, particularly for those samples that have traditionally posed a challenge. The newly proposed model, which can not only effectively outperform existing PAD solutions, but also achieve admirable flexibility and applicability to novel attack scenarios, truly demonstrates the power of the proposed teacher–student framework. This paves the way for improved security and trustworthiness in the area of face recognition systems and the deployment of biometric technologies. Full article

Telemedicine diagnosis has become a more flexible and convenient way to receive diagnoses, which is of great significance in enhancing diagnosis, cutting costs, and serving remote users. However, telemedicine faces many security problems, such as the complexity of user authentication, the balance of the existing biometric factor authentication scheme, the unpredictability of user behavior, and the difficulty of unified authentication due to the differences in the security standards and authentication mechanisms of different trust domains, which affect the sustainable development of telemedicine. To address the above issues, this paper presents an identity management scheme based on multi-factor authentication and dynamic trust evaluation for telemedicine. Its authentication combines iris recognition for secure biometric verification, smart cards for encrypted credential storage, and static passwords for supplementary verification, addressing scenarios like facial coverage in medical settings. The scheme dynamically adjusts authentication based on attack rates, login anomalies, and service durations. By integrating ShangMi cryptographic algorithms and blockchain, it optimizes performance, achieving 35% lower communication overhead than previous protocols. A security analysis shows it resists impersonation, man-in-the-middle, and password modification attacks while preserving user anonymity. System evaluation meets authoritative standards, validating its practicality. This scheme balances security and efficiency, providing a strong basis for telemedicine’s long-term viability. Full article

In recent years, the rapid growth of cryptocurrency markets has highlighted the urgent need for advanced security solutions capable of addressing a spectrum of unique threats, from phishing and wallet hacks to complex blockchain vulnerabilities. This paper presents a comprehensive approach to fortifying cryptocurrency systems by harnessing the structural symmetry inherent in transactional patterns. By leveraging local large language models (LLMs), embeddings, and vector databases, we develop an intelligent and scalable security expert system that exploits symmetry-based anomaly detection to enhance threat identification. Cryptocurrency networks face increasing threats from sophisticated attacks that often exploit asymmetric vulnerabilities. To counteract these risks, we propose a novel security expert system that integrates symmetry-aware analysis through LLMs and advanced embedding techniques. Our system efficiently captures symmetrical transaction patterns, enabling robust detection of anomalies and threats while preserving structural integrity. By integrating a modular framework with LangChain and a vector database (Chroma DB), we achieve improved accuracy, recall, and precision by leveraging the symmetry of transaction distributions and behavioral patterns. This work sets a new benchmark for LLM-driven cybersecurity solutions, offering a scalable and adaptive approach to reinforcing the security symmetry in cryptocurrency systems. The proposed expert system was evaluated using a benchmark dataset of cryptocurrency transactions, including real-world threat scenarios involving phishing, fraudulent transactions, and blockchain anomalies. The system achieved an accuracy of 92%, a precision of 89%, and a recall of 93%, demonstrating a 10% improvement over existing security frameworks. Compared to traditional rule-based and machine learning-based detection methods, our approach significantly enhances real-time threat detection while reducing false positives. The integration of LLMs with embeddings and vector retrieval enables more efficient contextual anomaly detection, setting a new benchmark for AI-driven security solutions in the cryptocurrency domain. Full article