Search Results (2,095)

The southern conifer Agathis australis (D.Don) Lindl. is a large and long-lived species endemic to Aotearoa New Zealand. It is threatened due to past logging activities, pathogen attack and potentially climate change, with increasing severity and frequency of drought and heatwaves across its distribution. Like many large tree species, little is known about the carbon dynamics of this ecologically and culturally significant species. We explored seasonal variations in non-structural carbohydrates (NSCs) and growth in trees ranging from 20 to 175 cm diameter at breast height (DBH). NSCs were seasonally stable with no measurable pattern across seasons. However, we found growth rates standardised to basal area and sapwood area (growth efficiency) declined with tree age and stem NSC concentrations (including total NSCs, sugars and starch) all increased as trees aged. Total NSC concentrations were 0.3%–0.6% dry mass for small trees and 0.8%–1.8% dry mass for larger trees, with strong relationships between DBH and total NSC, sugar and starch in stems but not roots. Cumulative growth efficiency across the two-year study period declined as tree size increased. Furthermore, there was an inverse relationship between growth efficiency across the two-year study period and NSC concentrations of stems. This relationship was driven by differences in carbon dynamics in trees of different sizes, with trees progressing to a more conservative carbon strategy as they aged. Simultaneously declining growth efficiency and increasing NSC concentrations as trees age could be evidence for active NSC accumulation to buffer against carbon starvation in larger trees. Our study provides new insights into changing carbon dynamics as trees age and may be evidence for active carbon accumulation in older trees. This may provide the key for understanding the role of carbon processes in tree longevity. Full article

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

To enhance the molten salt corrosion resistance of Ni200 alloy plasma arc welds, the welds were subjected to tensile deformation followed by heat treatment. The grain boundary character distribution (GBCD) was analyzed using electron backscatter diffraction (EBSD) in conjunction with orientation imaging microscopy (OIM). A constant-temperature corrosion test at 900 °C was conducted to evaluate the impact of GBCD on the corrosion resistance of the welds. Results demonstrated that after processing with 6% tensile deformation, and annealing at 950 °C for 30 min, the fraction of low-ΣCSL grain boundaries increased from 1.2% in the as-welded condition to 57.3%, and large grain clusters exhibiting Σ3ⁿ orientation relationships were formed. During the heat treatment, an increased number of recrystallization nucleation sites led to a reduction in average grain size from 323.35 μm to 171.38 μm. When exposed to a high-temperature environment of 75% Na₂SO₄-25% NaCl mixed molten salt, the corrosion behavior was characterized by intergranular attack, with oxidation and sulfidation reactions resulting in the formation of NiO and Ni₃S₂. The corrosion resistance of Grain boundary engineering (GBE)-treated samples was significantly superior to that of Non-GBE samples, with respective corrosion rates of 0.3397 mg/cm²·h and 0.8484 mg/cm²·h. These findings indicate that grain boundary engineering can effectively modulate the grain boundary character distribution in Ni200 alloy welds, thereby enhancing their resistance to molten salt corrosion. Full article

The integration of unmanned aerial vehicles (UAVs) into next-generation wireless networks greatly enhances the flexibility and efficiency of communication and distributed computation for ground mobile devices. Federated learning (FL) provides a privacy-preserving paradigm for device collaboration but remains highly vulnerable to poisoning attacks and is further challenged by the resource constraints and heterogeneous data common to UAV-assisted systems. Existing robust aggregation and anomaly detection methods often degrade in efficiency and reliability under these realistic adversarial and non-IID settings. To bridge these gaps, we propose FedULite, a lightweight and robust federated learning framework specifically designed for UAV-assisted environments. FedULite features unsupervised local representation learning optimized for unlabeled, non-IID data. Moreover, FedULite leverages a robust, adaptive server-side aggregation strategy that uses cosine similarity-based update filtering and dimension-wise adaptive learning rates to neutralize sophisticated data and model poisoning attacks. Extensive experiments across diverse datasets and adversarial scenarios demonstrate that FedULite reduces the attack success rate (ASR) from over 90% in undefended scenarios to below 5%, while maintaining the main task accuracy loss within 2%. Moreover, it introduces negligible computational overhead compared to standard FedAvg, with approximately 7% additional training time. Full article

In the deep integration process between digital infrastructure and new economic forms, structural imbalance between the evolution rate of cloud storage technology and the growth rate of data-sharing demands has caused systemic security vulnerabilities such as blurred data sovereignty boundaries and nonlinear surges in privacy leakage risks. Existing academic research indicates current proxy re-encryption schemes remain insufficient for cloud access control scenarios characterized by diversified user requirements and personalized permission management, thus failing to fulfill the security needs of emerging computing paradigms. To resolve these issues, a revocable attribute-based proxy re-encryption scheme supporting policy-hiding is proposed. Data owners encrypt data and upload it to the blockchain while concealing attribute values within attribute-based encryption access policies, effectively preventing sensitive information leaks and achieving fine-grained secure data sharing. Simultaneously, proxy re-encryption technology enables verifiable outsourcing of complex computations. Furthermore, the SM3 (SM3 Cryptographic Hash Algorithm) hash function is embedded in user private key generation, and key updates are executed using fresh random factors to revoke malicious users. Ultimately, the scheme proves indistinguishability under chosen-plaintext attacks for specific access structures in the standard model. Experimental simulations confirm that compared with existing schemes, this solution delivers higher execution efficiency in both encryption/decryption and revocation phases. Full article

AISI 347H stainless steel is widely used in high-temperature environments due to its excellent creep strength and oxidation resistance; however, its corrosion performance remains highly sensitive to thermal oxidation, and the effects of thermal history on its passive film stability are not yet fully understood. This study addresses this knowledge gap by systematically investigating the influence of solution treatment on the corrosion and oxidation resistance of AISI 347H stainless steel. The specimens were subjected to solution heat treatment at 1050 °C, followed by air cooling, and then evaluated through electrochemical testing, high-temperature oxidation experiments at 550 °C, and multiscale surface characterization techniques. The solution treatment refined the austenitic microstructure by dissolving coarse Nb-rich precipitates, as confirmed by SEM and EBSD, and improved passive film integrity. The stabilizing effect of Nb also played a critical role in suppressing sensitization, thereby enhancing resistance to intergranular attack. Electrochemical measurements and EIS analysis revealed a lower corrosion current density and higher charge transfer resistance in the treated samples, indicating enhanced passivation behavior. ToF-SIMS depth profiling and oxide thickness analysis confirmed a slower parabolic oxide growth rate and reduced oxidation rate constant in the solution-treated condition. At 550 °C, oxidation was suppressed by the formation of compact, Cr-rich scales with dual-distributed Nb oxides, effectively limiting diffusion pathways and stabilizing the protective layer. These findings demonstrate that solution treatment is an effective strategy to improve the long-term corrosion and oxidation performance of AISI 347H stainless steel in harsh service environments. Full article

Secure communication is critically dependent on high-speed and high-security quantum random number generation (QRNG). In this work, we present a responsive approach to enhance the efficiency and security of QRNG by leveraging polarization-controlled heterodyne detection to simultaneously measure the quadrature amplitude and phase fluctuations of vacuum shot noise. To address the practical non-idealities inherent in QRNG systems, we investigate the critical impacts of imbalanced heterodyne detection, amplitude–phase overlap, finite-size effects, and security parameters on quantum conditional min-entropy derived from the entropy uncertainty principle. It effectively mitigates the overestimation of randomness and fortifies the system against potential eavesdropping attacks. For a high-security parameter of ${10}^{-20}$, QRNG achieves a true random bit extraction ratio of 83.16% with a corresponding real-time speed of 37.25 Gbps following a 16-bit analog-to-digital converter quantization and 1.4 GHz bandwidth extraction. Furthermore, we develop a deep convolutional neural network for rapid and accurate entropy evaluation. The entropy evaluation of 13,473 sets of quadrature data is processed in 68.89 s with a mean absolute percentage error of 0.004, achieving an acceleration of two orders of magnitude in evaluation speed. Extracting the shot noise with full detection bandwidth, the generation rate of QRNG using dual-quadrature heterodyne detection exceeds 85 Gbps. The research contributes to advancing the practical deployment of QRNG and expediting rapid entropy assessment. Full article

With the growing number and diversity of network attacks, traditional security measures such as firewalls and data encryption are no longer sufficient to ensure robust network protection. As a result, intrusion detection systems (IDSs) have become a vital component in defending against evolving cyber threats. Although many modern IDS solutions employ machine learning techniques, they often suffer from low detection rates and depend heavily on manual feature engineering. Furthermore, most IDS models are designed to identify only a limited set of attack types, which restricts their effectiveness in practical scenarios where a network may be exposed to a wide array of threats. To overcome these limitations, we propose a novel approach to IDSs by implementing a combined dataset framework based on an enhanced hybrid principal component analysis–Transformer (PCA–Transformer) model, capable of detecting 21 unique classes, comprising 1 benign class and 20 distinct attack types across multiple datasets. The proposed architecture incorporates enhanced preprocessing and feature engineering, followed by the vertical concatenation of the CSE-CIC-IDS2018 and CICIDS2017 datasets. In this design, the PCA component is responsible for feature extraction and dimensionality reduction, while the Transformer component handles the classification task. Class imbalance was addressed using class weights, adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN). Experimental results show that the model achieves 99.80% accuracy for binary classification and 99.28% for multi-class classification on the combined dataset (CSE-CIC-IDS2018 and CICIDS2017), 99.66% accuracy for binary classification and 99.59% for multi-class classification on the CSE-CIC-IDS2018 dataset, 99.75% accuracy for binary classification and 99.51% for multi-class classification on the CICIDS2017 dataset, and 99.98% accuracy for binary classification and 98.01% for multi-class classification on the NF-BoT-IoT-v2 dataset, significantly outperforming existing approaches by distinguishing a wide range of classes, including benign and various attack types, within a unified detection framework. Full article

This paper presents an attack detection, response, and recommendation framework designed to protect the integrity and operational continuity of IoT-based critical infrastructure, specifically focusing on an energy use case. With the growing deployment of IoT-enabled smart meters in energy systems, ensuring data integrity is essential. The proposed framework monitors smart meter data in real time, identifying deviations that may indicate data tampering or device malfunctions. The system comprises two main components: an attack detection and prediction module based on machine learning (ML) models and a response and adaptation module that recommends countermeasures. The detection module employs a forecasting model using a long short-term memory (LSTM) architecture, followed by a dense layer to predict future readings. It also integrates a statistical thresholding technique based on Tukey’s fences to detect abnormal deviations. The system was evaluated on real smart meter data in a testbed environment. It achieved accurate forecasting (MAPE < 2% in most cases) and successfully flagged injected anomalies with a low false positive rate, an effective result given the lightweight, unsupervised, and real-time nature of the approach. These findings confirm the framework’s applicability in resource-constrained energy systems requiring real-time cyberattack detection and mitigation. Full article

To address the limitations of traditional hardened concrete road surfaces in coal mine tunnels, which are prone to damage and entail high maintenance costs, this study proposes using modular concrete blocks composed of fly ash and coal gangue as an alternative to conventional materials. These blocks offer advantages including ease of construction and rapid, straightforward maintenance, while also facilitating the reuse of substantial quantities of solid waste, thereby mitigating resource wastage and environmental pollution. Initially, the mineral composition of the raw materials was analyzed, confirming that although the physical and chemical properties of Liangshui Well coal gangue are slightly inferior to those of natural crushed stone, they still meet the criteria for use as concrete aggregate. For concrete blocks incorporating 20% fly ash, the steam curing process was optimized with a recommended static curing period of 16–24 h, a temperature ramp-up rate of 20 °C/h, and a constant temperature of 50 °C maintained for 24 h to ensure optimal performance. Orthogonal experimental analysis revealed that fly ash content exerted the greatest influence on the compressive strength of concrete, followed by the additional water content, whereas the aggregate particle size had a comparatively minor effect. The optimal mix proportion was identified as 20% fly ash content, a maximum aggregate size of 20 mm, and an additional water content of 70%. Performance testing indicated that the fabricated blocks exhibited a compressive strength of 32.1 MPa and a tensile strength of 2.93 MPa, with strong resistance to hydrolysis and sulfate attack, rendering them suitable for deployment in weakly alkaline underground environments. Considering the site-specific conditions of the Liangshuijing coal mine, ANSYS 2020 was employed to simulate and analyze the mechanical behavior of the blocks under varying loads, thicknesses, and dynamic conditions. The findings suggest that hexagonal coal gangue blocks with a side length of 20 cm and a thickness of 16 cm meet the structural requirements of most underground mine tunnels, offering a reference model for cost-effective paving and efficient roadway maintenance in coal mines. Full article

The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in machine learning-based IDSs, as it reduces the input dimensionality, enhances the detection accuracy, and lowers the computational latency. This paper introduces a novel optimization framework called Quantum Epigenetic Algorithm (QEA), which synergistically combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection. The algorithm balances global exploration and local exploitation by leveraging quantum superposition for diverse candidate generation while dynamically adjusting gene expression through an epigenetic activation mechanism. A multi-objective fitness function guides the search process by optimizing the detection accuracy, false positive rate, inference latency, and model compactness. The QEA was evaluated across four benchmark datasets—UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT—and consistently outperformed baseline methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Genetic Algorithm (QGA). Notably, QEA achieved the highest classification accuracy (up to 97.12%), the lowest false positive rates (as low as 1.68%), and selected significantly fewer features (e.g., 18 on TON_IoT) while maintaining near real-time latency. These results demonstrate the robustness, efficiency, and scalability of QEA for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments. Full article

Advanced Persistent Threat (APT) attacks pose a serious challenge to traditional detection methods. These methods often suffer from high false-alarm rates and limited accuracy due to the multi-stage and covert nature of APT attacks. In this paper, we propose TSE-APT, a time-series ensemble model that addresses these two limitations. It combines multiple machine-learning models, such as Random Forest (RF), Multi-Layer Perceptron (MLP), and Bidirectional Long Short-Term Memory Network (BiLSTM) models, to dynamically capture correlations between multiple stages of the attack process based on time-series features. It discovers hidden features through the integration of multiple machine-learning models to significantly improve the accuracy and robustness of APT detection. First, we extract a collection of dynamic time-series features such as traffic mean, flow duration, and flag frequency. We fuse them with static contextual features, including the port service matrix and protocol type distribution, to effectively capture the multi-stage behaviors of APT attacks. Then, we utilize an ensemble-learning model with a dynamic weight-allocation mechanism using a self-attention network to adaptively adjust the sub-model contribution. The experiments showed that using time-series feature fusion significantly enhanced the detection performance. The RF, MLP, and BiLSTM models achieved 96.7% accuracy, considerably enhancing recall and the false positive rate. The adaptive mechanism optimizes the model’s performance and reduces false-alarm rates. This study provides an analytical method for APT attack detection, considering both temporal dynamics and context static characteristics, and provides new ideas for security protection in complex networks. Full article

The palm-vein recognition system has garnered attention as a biometric technology due to its resilience to external environmental factors, protection of personal privacy, and low risk of external exposure. However, with recent advancements in deep learning-based generative models for image synthesis, the quality and sophistication of fake images have improved, leading to an increased security threat from counterfeit images. In particular, palm-vein images acquired through near-infrared illumination exhibit low resolution and blurred characteristics, making it even more challenging to detect fake images. Furthermore, spoof detection specifically targeting palm-vein images has not been studied in detail. To address these challenges, this study proposes the Fourier-gated feature-fusion network (FGFNet) as a novel spoof detector for palm-vein recognition systems. The proposed network integrates masked fast Fourier transform, a map-based gated feature fusion block, and a fast Fourier convolution (FFC) attention block with global contrastive loss to effectively detect distortion patterns caused by generative models. These components enable the efficient extraction of critical information required to determine the authenticity of palm-vein images. In addition, fractal dimension estimation (FDE) was employed for two purposes in this study. In the spoof attack procedure, FDE was used to evaluate how closely the generated fake images approximate the structural complexity of real palm-vein images, confirming that the generative model produced highly realistic spoof samples. In the spoof detection procedure, the FDE results further demonstrated that the proposed FGFNet effectively distinguishes between real and fake images, validating its capability to capture subtle structural differences induced by generative manipulation. To evaluate the spoof detection performance of FGFNet, experiments were conducted using real palm-vein images from two publicly available palm-vein datasets—VERA Spoofing PalmVein (VERA dataset) and PLUSVein-contactless (PLUS dataset)—as well as fake palm-vein images generated based on these datasets using a cycle-consistent generative adversarial network. The results showed that, based on the average classification error rate, FGFNet achieved 0.3% and 0.3% on the VERA and PLUS datasets, respectively, demonstrating superior performance compared to existing state-of-the-art spoof detection methods. Full article

The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems (IDS), thereby compromising reliability. We propose Feature-Importance GAN SMOTE (FIGS), an innovative, realistic intrusion-detection framework designed for IoT environments to address this challenge. Unlike other works that rely only on traditional oversampling methods, FIGS integrates sensitivity-based feature-importance analysis, Generative Adversarial Network (GAN)-based augmentation, a novel imbalance ratio (GIR), and Synthetic Minority Oversampling Technique (SMOTE) for generating high-quality synthetic data for minority classes. FIGS enhanced minority class detection by focusing on the most important features identified by the sensitivity analysis, while minimizing computational overhead and reducing noise during data generation. Evaluations on the CICIoMT2024 and CICIDS2017 datasets demonstrate that FIGS improves detection accuracy and significantly lowers the false negative rate. FIGS achieved a 17% improvement over the baseline model on the CICIoMT2024 dataset while maintaining performance for the majority groups. The results show that FIGS represents a highly effective solution for real-world IoT networks with high detection accuracy across all classes without introducing unnecessary computational overhead. Full article

The proliferation of Internet of Things (IoT) swarms—comprising billions of low-end interconnected embedded devices—has transformed industrial automation, smart homes, and agriculture. However, these swarms are highly susceptible to firmware anomalies that can propagate across nodes, posing serious security threats. To address this, we propose a novel Remote Attestation (RA) framework for real-time firmware verification, leveraging Relational Graph Neural Networks (RGNNs) to model the graph-like structure of IoT swarms and capture complex inter-node dependencies. Unlike conventional Graph Neural Networks (GNNs), RGNNs incorporate edge types (e.g., Prompt, Sensor Data, Processed Signal), enabling finer-grained detection of propagation dynamics. The proposed method uses runtime Static Random Access Memory (SRAM) data to detect malicious firmware and its effects without requiring access to firmware binaries. Experimental results demonstrate that the framework achieves 99.94% accuracy and a 99.85% anomaly detection rate in a 4-node swarm (Swarm-1), and 100.00% accuracy with complete anomaly detection in a 6-node swarm (Swarm-2). Moreover, the method proves resilient against noise, dropped responses, and trace replay attacks, offering a robust and scalable solution for securing IoT swarms. Full article