Search Results (14,667)

With the growing intensity of global cybersecurity threats and the rapid advancement of attack techniques, strengthening enterprise information and communication technology (ICT) infrastructures and enhancing digital forensics have become critical imperatives. Cloud environments, in particular, present substantial challenges due to the limited availability of effective forensic tools and the pressing demand for impartial and legally admissible digital evidence. To address these challenges, we propose a proactive digital forensics mechanism (P-DFM) designed for emergency incident management in enterprise settings. This mechanism integrates a range of forensic tools to identify and preserve critical digital evidence. It also incorporates the MITRE ATT&CK framework with Security Information and Event Management (SIEM) and Managed Detection and Response (MDR) systems to enable comprehensive and timely threat detection and analysis. The principal contribution of this study is the formulation of a novel Proactive Digital Evidence Forensics Standard Operating Procedure (P-DEFSOP), which enhances the accuracy and efficiency of security threat detection and forensic analysis while ensuring that digital evidence remains legally admissible. This advancement significantly reinforces the cybersecurity posture of enterprise networks. Our approach is systematically grounded in the Digital Evidence Forensics Standard Operating Procedure (DEFSOP) framework and complies with internationally recognized digital forensic standards, including ISO/IEC 27035 and ISO/IEC 27037, to ensure the integrity, reliability, validity, and legal admissibility of digital evidence throughout the forensic process. Given the complexity of cloud computing infrastructures—such as Chunghwa Telecom HiCloud, Amazon Web Services (AWS), Google Cloud, and Microsoft Azure—we underscore the critical importance of impartial and standardized digital forensic services in cloud-based environments. Full article

Adversarial examples pose a severe threat to deep neural networks. They are crafted by applying imperceptible perturbations to benign inputs, causing the model to produce incorrect predictions. Most existing attack methods exhibit limited generalization, especially in black-box settings involving unseen models or unknown classes. To address these limitations, we propose MAAG (multi-attention adversarial generation), a novel model architecture that enhances attack generalizability and transferability. MAAG integrates channel and spatial attention to extract representative features for adversarial example generation and capture diverse decision boundaries for better transferability. A composite loss guides the generation of adversarial examples across different victim models. Extensive experiments validate the superiority of our proposed method in crafting adversarial examples for both known and unknown classes. Specifically, it surpasses existing generative methods by approximately 7.0% and 7.8% in attack success rate on known and unknown classes, respectively. Full article

In the digital era where images are easily accessible, concerns about image authenticity and integrity are increasing. To address this, we propose a deep learning-based fragile watermarking method for secure image authentication and content recovery. The method utilizes bottleneck features extracted by the convolutional encoder to carry both authentication and recovery information and employs deconvolution at the decoder to reconstruct image content. Additionally, the Arnold Transform is applied to scramble feature information, effectively enhancing resistance to collage attacks. At the detection stage, block voting and morphological closing operations improve tamper localization accuracy and robustness. Experiments tested various tampering ratios, with performance evaluated by PSNR, SSIM, precision, recall, and F1-score. Experiments under varying tampering ratios demonstrate that the proposed method maintains high visual quality and achieves reliable tamper detection and recovery, even at 75% tampering. Evaluation metrics including PSNR, SSIM, precision, recall, and F1-score confirm the effectiveness and practical applicability of the method. Full article

This study advances alkali-activated cementitious materials (AACMs) by developing a ground-granulated blast furnace slag/high-calcium fly ash (GGBS/HCFA) composite that incorporates Tuokexun desert sand and by establishing a clear linkage between activator chemistry, mix proportions, curing regimen, and microstructural mechanisms. The innovation lies in valorizing industrial by-products and desert sand while systematically optimizing the aqueous glass modulus, alkali equivalent, HCFA dosage, and curing temperature/time, and coupling mechanical testing with XRD/FTIR/SEM to reveal performance–structure relationships under thermal and chemical attacks. The optimized binder (aqueous glass modulus 1.2, alkali equivalent 6%, and HCFA 20%) achieved 28-day compressive and flexural strengths of 52.8 MPa and 9.5 MPa, respectively; increasing HCFA beyond 20% reduced compressive strength, while flexural strength peaked at 20%. The preferred curing condition was 70 °C for 12 h. Characterization showed C-(A)-S-H as the dominant gel; elevated temperature led to its decomposition, acid exposure produced abundant CaSO₄, and NaOH exposure formed N-A-S-H, each correlating with strength loss. Quantitatively, acid resistance was weaker than alkali resistance and both deteriorated with concentration: in H₂SO₄, 28-day mass loss rose from 1.22% to 4.16%, with compressive/flexural strength retention dropping to 75.2%, 71.2%, 63.4%, and 57.4% and 65.3%, 61.6%, 58.9%, and 49.5%, respectively; in NaOH (0.2/0.5/0.8/1.0 mol/L), 28-day mass change was +0.74%, +0.88%, −1.85%, and −2.06%, compressive strength declined in all cases (smallest drop 7.77% at 0.2 mol/L), and flexural strength increased at lower alkalinity, consistent with a pore-filling micro-densification effect before gel dissolution/cracking dominates. Practically, the recommended mix and curing window deliver structural-grade performance while improving high-temperature and acid/alkali resistance relative to non-optimized formulations, offering a scalable, lower-carbon route to utilize regional desert sand and industrial wastes in durable cementitious applications. Full article

Hyperspectral anomaly detection (HAD) aims to separate subtle anomalies of a given hyperspectral image (HSI) from its background, which is a hot topic as well as a challenging inverse problem. Despite the significant success of the deep learning-based HAD methods, they are hard to interpret due to their black-box nature. Meanwhile, deep learning methods suffer from the identity mapping (IM) problem, referring to the network excessively focusing on the precise reconstruction of the background while neglecting the appropriate representation of anomalies. To this end, this paper proposes a self-attention-aided deep unfolding tensor representation network (SATRNet) for interpretable HAD by solving the tensor representation (TR)-based optimization model within the framework of deep networks. In particular, a Self-Attention Learning Module (SALM) was first designed to extract discriminative features of the input HSI. The HAD problem was then formulated as a tensor representation problem by exploring both the low-rankness of the background and the sparsity of the anomaly. A Weight Learning Module (WLM) exploring local details was also generated for precise background reconstruction. Finally, a deep network was built to solve the TR-based problem through unfolding and parameterizing the iterative optimization algorithm. The proposed SATRNet prevents the network from learning meaningless mappings, making the network interpretable to some extent while essentially solving the IM problem. The effectiveness of the proposed SATRNet is validated on 11 benchmark HSI datasets. Notably, the performance of SATRNet against adversarial attacks is also investigated in the experimentation, which is the first work exploring adversarial robustness in HAD to the best of our knowledge. Full article

Background: Advances in stent grafts and endovascular techniques have expanded the indications for thoracic endovascular aortic repair (TEVAR) to include arch lesions. In situ needle fenestration (ISNF) has emerged as a promising technique for revascularizing supra-aortic branches. The aim of this study is to evaluate the safety and efficacy of triple in situ needle fenestration during TEVAR for aortic arch pathologies in a single-center experience. Materials and Methods: A retrospective analysis was conducted on fifteen patients who underwent in situ triple fenestration TEVAR between June 2023 and March 2024. The median age of the patients was 51,33 years (±19.69) and twelve of the patients were male. All procedures were performed under general anesthesia in a hybrid operating room. Ethical approval was obtained from the institutional review board, and informed consent was received from all participants. Results: Primary technical success was achieved in all cases (15/15, 100%). The mean operation time was 197.33 min (range: 126–302). Two patients experienced a minor hematoma at the access site. Mortality was observed in one patient (6.66%) during the 30-day follow-up period. The total hospital stay averaged 7 ± 3.36 days. One patient had a transient ischemic attack, but there were no incidents of stroke or spinal cord ischemia. No procedure-related endoleak was observed during the intervention; however, eight patients required reintervention in the descending aorta. Conclusions: ISNF may be an effective and feasible method for revascularizing arch vessels, with low rates of early mortality and stroke when performed by experienced practitioners. However, larger multicenter studies with longer follow-up are needed to confirm the durability and long-term outcomes of this technique. Full article

In vision-guided autonomous robots, object detectors play a crucial role in perceiving the environment for path planning and decision-making. However, adaptive adversarial patch attacks undermine the resilience of detector-based systems. Strengthening object detectors against such adaptive attacks enhances the robustness of navigation systems. Existing defenses against patch attacks are primarily designed for stationary scenes and struggle against adaptive patch attacks that vary in scale, position, and orientation in dynamic environments. In this paper, we introduce Ad_YOLO+, an efficient and effective plugin that extends Ad_YOLO to defend against white-box patch-based image attacks. Built on YOLOv5x with an additional patch detection layer, Ad_YOLO+ is trained on a specially crafted adversarial dataset (COCO-Visdrone-2019). Unlike conventional methods that rely on redundant image preprocessing, our approach directly detects adversarial patches and the overlaid objects. Experiments on the adversarial training dataset demonstrate that Ad_YOLO+ improves both provable robustness and clean accuracy. Ad_YOLO+ achieves $85.4\%$ top-1 clean accuracy on the COCO dataset and $74.63\%$ top-1 robust provable accuracy against pixel square patches anywhere on the image for the COCO-VisDrone-2019 dataset. Moreover, under adaptive attacks in AirSim simulations, Ad_YOLO+ reduces the attack success rate, ensuring tracking resilience in both dynamic and static settings. Additionally, it generalizes well to other patch detection weight configurations. Full article

Software-defined networking (SDN) is a transformative approach for managing modern network architectures, particularly in Internet-of-Things (IoT) applications. However, ensuring the optimal SDN performance and security often needs a robust sensitivity analysis (SA). To complement existing SA methods, this study proposes a new SA framework that integrates design of experiments (DOE) and machine-learning (ML) techniques. Although existing SA methods have been shown to be effective and scalable, most of these methods have yet to hybridize anomaly detection and classification (ADC) and data augmentation into a single, unified framework. To fill this gap, a targeted application of well-established existing techniques is proposed. This is achieved by hybridizing these existing techniques to undertake a more robust SA of a typified SDN-reliant IoT network. The proposed hybrid framework combines Latin hypercube sampling (LHS)-based DOE and generative adversarial network (GAN)-driven data augmentation to improve SA and support ADC in SDN-reliant IoT networks. Hence, it is called DOE-GAN-SA. In DOE-GAN-SA, LHS is used to ensure uniform parameter sampling, while GAN is used to generate synthetic data to augment data derived from typified real-world SDN-reliant IoT network scenarios. DOE-GAN-SA also employs a classification and regression tree (CART) to validate the GAN-generated synthetic dataset. Through the proposed framework, ADC is implemented, and an artificial neural network (ANN)-driven SA on an SDN-reliant IoT network is carried out. The performance of the SDN-reliant IoT network is analyzed under two conditions: namely, a normal operating scenario and a distributed-denial-of-service (DDoS) flooding attack scenario, using throughput, jitter, and response time as performance metrics. To statistically validate the experimental findings, hypothesis tests are conducted to confirm the significance of all the inferences. The results demonstrate that integrating LHS and GAN significantly enhances SA, enabling the identification of critical SDN parameters affecting the modeled SDN-reliant IoT network performance. Additionally, ADC is also better supported, achieving higher DDoS flooding attack detection accuracy through the incorporation of synthetic network observations that emulate real-time traffic. Overall, this work highlights the potential of hybridizing LHS-based DOE, GAN-driven data augmentation, and ANN-assisted SA for robust network behavioral analysis and characterization in a new hybrid framework. Full article

We propose a Transformer-based data augmentation framework with a time-series dual-stream architecture to address performance degradation in encrypted network traffic classification caused by class imbalance between attack and benign traffic. The proposed framework independently processes the complete flow’s sequential packet information and statistical characteristics by extracting and normalizing a local channel (comprising packet size, inter-arrival time, and direction) and a set of six global flow-level statistical features. These are used to generate a fixed-length multivariate sequence and an auxiliary vector. The sequence and vector are then fed into an encoder-only Transformer that integrates learnable positional embeddings with a FiLM + context token-based injection mechanism, enabling complementary representation of sequential patterns and global statistical distributions. Large-scale experiments demonstrate that the proposed method reduces reconstruction RMSE and additional feature restoration MSE by over 50%, while improving accuracy, F1-Score, and AUC by 5–7%p compared to classification on the original imbalanced datasets. Furthermore, the augmentation process achieves practical levels of processing time and memory overhead. These results show that the proposed approach effectively mitigates class imbalance in encrypted traffic classification and offers a promising pathway to achieving more robust model generalization in real-world deployment scenarios. Full article

With recent advancements in digital environments, user authentication is becoming increasingly important. Traditional authentication methods such as passwords and PINs suffer from inherent limitations, including vulnerability to theft, guessing, and replay attacks. Consequently, there has been a growing body of research on more accurate and efficient user authentication methods. One such approach involves the use of biometric signals to enhance security. However, biometric methods face significant challenges in ensuring stable authentication accuracy, primarily due to variations in the user’s environment, physical activity, and health conditions. To address these issues, this paper proposes a biometric-signal-based user authentication system using graph neural networks (GNNs). The feasibility of the proposed system was evaluated using an electromyogram (EMG) dataset specifically constructed by Chosun University for user authentication research. GNNs have demonstrated exceptional performance in modeling the relationships among complex data and attracted attention in various fields. Specifically, GNNs are well-suited for modeling user behavioral patterns while considering temporal and spatial relationships, making them an ideal method for adapting to dynamic and evolving user patterns. Unlike traditional neural networks, GNNs can dynamically learn and adapt to changes or evolutions in user behavioral patterns over time. This paper describes the design and implementation of a user authentication system using GNNs with an EMG dataset and discusses how the system can adapt to dynamic and changing user patterns. Full article

Deep neural networks (DNNs) excel in image classification but are vulnerable to backdoor attacks due to reliance on external training data, where specific markers trigger preset misclassifications. Existing attack techniques have an obvious trade-off between the effectiveness of the triggers and the stealthiness, which limits their practical application. For this purpose, in this paper, we develop a method—chromatic channel-based implicit backdoor attack (CCIBA), which combines a discrete wavelet transform (DWT) and singular value decomposition (SVD) to embed triggers in the frequency domain through the chromaticity properties of the YUV color space. Experimental validation on different image datasets shows that compared to existing methods, CCIBA can achieve a higher attack success rate without a large impact on the normal classification ability of the model, and its good stealthiness is verified by manual detection as well as different experimental metrics. It successfully circumvents existing defense methods in terms of sustainability. Overall, CCIBA strikes a balance between covertness, effectiveness, robustness and sustainability. Full article

In dynamic and open cloud service processes, particularly in distributed networked manufacturing environments, the complex and volatile manufacturing landscape introduces numerous uncertainties and disturbances. This paper addresses the common issue of cloud resource connection interruptions by proposing a path substitution strategy based on alternative service routes. By integrating agent-based simulation and complex network methodologies, a simulation model for evaluating the robustness of cloud manufacturing service systems is developed, enabling dynamic simulation and quantitative decision-making for the proposed robustness enhancement strategies. First, a hybrid modeling approach for cloud manufacturing service systems is proposed to meet the needs of robustness analysis. The specific construction of the hybrid simulation model is achieved using the AnyLogic 8.7.4 simulation software and Java-based secondary development techniques. Second, a complex network model focusing on cloud manufacturing resource entities is further constructed based on the simulation model. By combining the two models, two-dimensional robustness evaluation indicators—comprising performance robustness and structural robustness—are established. Then, four types of edge attack strategies are designed based on the initial topology and recomputed topology. To ensure system operability after edge failures, a path substitution strategy is proposed by introducing redundant routes. Finally, a case study of a cloud manufacturing project is conducted. The results show the following: (1) The proposed robustness evaluation model fully captures complex disturbance scenarios in cloud manufacturing, and the designed simulation experiments support the evaluation and comparative analysis of robustness improvement strategies from both performance and structural robustness dimensions. (2) The path substitution strategy significantly enhances the robustness of cloud manufacturing services, though its effects on performance and structural robustness vary across different disturbance scenarios. Full article

Improvements in the aerodynamic performance of the H-Darrieus turbine are crucial to address future energy requirements. This work aims to optimize the behavior of two adjacent turbines through the addition of a dual H-Darrieus rotor. The first rotor is composed of three NACA 0021 blades, while the second comprises a single Eppler 420 blade. This study focuses on 2D CFD simulation based on the solution of the unsteady Reynolds-averaged Navier–Stokes (URANS) equations, using the sliding mesh method and $k-\omega$ SST turbulence model. The simulation results indicate a 17% improvement in the efficiency of the two turbines integrating dual rotors, compared to the two isolated turbines, for $\alpha$ = 0°. Moreover, the power coefficient ${ (C}_P)$ reaches maximum values of 0.49, 0.42, and 0.40 for angles of attack of 30°, 25°, and 20°, respectively, at TSR = 2.51. Conversely, the selection of an optimal angle of attack allows the efficiency of the two H-Darrieus turbines to be increased. It is also shown by the results that the effect of stagnation is reduced and lift is maximized when the optimum distance between two adjacent turbines is chosen. Moreover, the overall aerodynamic performance of the system is enhanced by the potential of a dual-rotor configuration, and the wake between the two turbines is disrupted, which can result in a decrease in energy production within wind farms. Full article

Basalt fibers possess high tensile strength and excellent corrosion resistance, properties that may enhance the chloride resistance of recycled aggregate concrete (RAC) structures. Nevertheless, the effects of basalt fibers on RAC structures under chloride attack remain poorly understood. This study investigates mass loss and the deterioration of key mechanical properties in basalt fiber-reinforced RAC composite slab–column assemblies (RAC composite assemblies) subjected to NaCl freeze–thaw cycles (F-Cs) and dry–wet alternations (D-As) and further explores the damage mechanisms of the concrete matrix through microscopic characterization. The results show that, compared with NaCl F-Cs, NaCl D-As have a more pronounced impact on the performance degradation of RAC composite slab–column assemblies. Moreover, basalt fibers effectively mitigate the deterioration of RAC composite assemblies in chloride-rich environments, particularly under NaCl D-As, where their protective effect is more evident. At 2.5 vol% fiber content, impact toughness peaked at an 83.7% improvement after 30 D-As, while flexural toughness showed a maximum enhancement of 773.6% after 100 F-Cs. Scanning electron microscopy energy-dispersive spectroscopy (SEM-EDS) analysis revealed a marked increase in Cl content within RAC, with NaCl D-As causing more severe erosion than NaCl F-Cs. Additionally, basalt fibers significantly inhibited chloride ion penetration and associated erosion in RAC. These findings provide valuable insights into utilizing basalt fibers to enhance the durability of RAC in coastal infrastructure exposed to chloride attacks. Further research on long-term performance and fiber parameter optimization is needed to support practical implementation. Full article

Distributed denial-of-service (DDoS) attacks are a prevalent threat to resource-constrained IoT deployments. We present an edge-based detection and mitigation system integrated with the oneM2M architecture. By using a Raspberry Pi 4 client and five Raspberry Pi 3 attack nodes in a smart-home testbed, we collected 200,000 packets with 19 features across four traffic states (normal, SYN/UDP/ICMP floods), trained Decision Tree, 2D-CNN, and LSTM models, and deployed the best model on an edge computer for real-time inference. The edge node classifies traffic and triggers per-attack defenses on the device (SYN cookies, UDP/ICMP iptables rules). On a held-out test set, the 2D-CNN achieved 98.45% accuracy, outperforming the LSTM (96.14%) and Decision Tree (93.77%). In end-to-end trials, the system sustained service during SYN floods (time to capture 200 packets increased from 5.05 s to 5.51 s after enabling SYN cookies), mitigated ICMP floods via rate limiting, and flagged UDP floods for administrator intervention due to residual performance degradation. These results show that lightweight, edge-deployed learning with targeted controls can harden oneM2M-based IoT systems against common DDoS vectors. Full article