Search Results (2,442)

This paper proposes a residual network model integrated with an attention mechanism module for the detection and classification of malware on railway mobile terminals. To address the issues of insufficient and imbalanced samples, Wasserstein Generative Adversarial Networks (WGANs) are utilized to synthesize grayscale image data of malware with high similarity to real samples. The performance of the model is evaluated on the publicly available CIC-InvesAndMal2019 dataset and an extended balanced dataset. Experimental results demonstrate that the synergistic integration of residual networks, WGANs, and attention mechanisms significantly enhances the performance of the malware detection model. In the context of railway applications, the proposed approach also achieves favorable classification performance when applied to image datasets derived from malware samples of railway mobile terminals. Multiple ablation studies are conducted to individually validate the contributions of each technical component in improving the classification model’s efficacy. The adoption of the SE-ResNet architecture combined with WGAN-based data augmentation presents a practical and efficient technical solution. Full article

In the Internet of Vehicles (IoV), zero-day attacks pose a significant security threat. These attacks are characterized by unknown patterns and limited sample availability. Traditional anomaly detection methods often fail because they rely on oversimplified assumptions, hindering their ability to model complex normal IoV behavior. This limitation results in low detection accuracy and high false alarm rates. To overcome these challenges, we propose a novel zero-day attack detection framework based on Feature-Enhanced Inverse Mapping (IM-ZDD). The framework introduces a two-stage process. In the first stage, a feature enhancement module mitigates data scarcity by employing an innovative multi-generator, multi-discriminator Conditional GAN (CGAN) with dynamic focusing loss to generate a large-scale, high-quality synthetic normal dataset characterized by sharply defined feature boundaries. In the second stage, a learning-based inverse mapping module is trained exclusively on this synthetic data. Through adversarial training, the module learns a precise inverse mapping function, thereby establishing a compact and expressive representation of normal behavior. During detection, samples that cannot be effectively mapped are identified as attacks. Experimental results on the F2MD platform show IM-ZDD achieves superior accuracy and a low false alarm rate, yielding an average AUC of 98.25% and F1-Score of 96.41%, surpassing state-of-the-art methods by up to 4.4 and 10.8 percentage points. Moreover, with a median detection latency of only 3 ms, the framework meets real-time requirements, providing a robust solution for zero-day attack detection in data-scarce IoV environments. Full article

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments. Full article

Autoimmune diseases constitute a broadly prevalent category of disorders. Conventional computer-aided diagnostic (CAD) techniques rely on large volumes of data paired with reliable annotations. However, the diverse symptomatology and diagnostic complexity of autoimmune diseases result in a scarcity of reliably labeled biological samples. In this study, we propose a pseudo-label-based conditional domain adversarial network (CDAN-PL) framework by integrating Raman spectroscopy with domain adaptation technology, enabling label-free unsupervised transfer diagnosis of diseases. Compared to traditional unsupervised domain adaptation techniques, our CDAN-PL framework generates reliable pseudo-labels to ensure the robust implementation of conditional adversarial methods. Additionally, its spectral data-adaptive feature extraction techniques further solidify the model’s superiority in Raman spectroscopy-based disease diagnosis. CDAN-PL exhibits excellent performance in homologous transfer tasks, achieving an average accuracy of 92.3%—surpassing the baseline models’ 80.81% and 86.4%. Moreover, it attains an average accuracy of 90.05% in non-homologous transfer tasks, further validating its generalization capability. Full article

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article

Artificial intelligence-based image processing is critical for sensor fusion and image transformation in mobility systems. Advanced driver assistance functions such as forward monitoring and digital side mirrors are essential for driving safety. Degradation due to raindrops, fog, and high-dynamic range (HDR) imbalance caused by lighting changes impairs visibility and reduces object recognition and distance estimation accuracy. This paper proposes a diffusion framework to enhance visibility under multi-degradation conditions. The denoising diffusion probabilistic model (DDPM) offers more stable training and high-resolution restoration than the generative adversarial networks. The DDPM relies on large-scale paired datasets, which are difficult to obtain in raindrop scenarios. This framework applies the Palette diffusion model, comprising data augmentation and raindrop-removal modules. The data augmentation module generates raindrop image masks and learns inpainting-based raindrop synthesis. Synthetic masks simulate raindrop patterns and HDR imbalance scenarios. The raindrop-removal module reconfigures the Palette architecture for image-to-image translation, incorporating the augmented synthetic dataset for raindrop removal learning. Loss functions and normalization strategies improve restoration stability and removal performance. During inference, the framework operates with a single conditional input, and an efficient sampling strategy is introduced to significantly accelerate the process. In post-processing, tone adjustment and chroma compensation enhance visual consistency. The proposed method preserves fine structural details and outperforms existing approaches in visual quality, improving the robustness of vision systems under adverse conditions. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

This review explores the current landscape of neural network-based methods for digital image noise processing. Digital cameras have become ubiquitous in fields like forensics and medical diagnostics, and image noise remains a critical factor for ensuring image quality. Traditional noise suppression techniques are often limited by extensive parameter selection and inefficient handling of complex data. In contrast, neural networks, particularly convolutional neural networks, autoencoders, and generative adversarial networks, have shown significant promise for noise estimation, suppression, and analysis. These networks can handle complex noise patterns, leverage context-specific data, and adapt to evolving conditions with minimal manual intervention. This paper describes the basics of camera and image noise components and existing techniques for their evaluation. Main neural network-based methods for noise estimation are briefly presented. This paper discusses neural network application for noise suppression, classification, image source identification, and the extraction of unique camera fingerprints through photo response non-uniformity. Additionally, it highlights the challenges of generating reliable training datasets and separating image noise from photosensor noise, which remains a fundamental issue. Full article

GPS spoofing remains a critical threat in the use of autonomous vehicles. Machine-learning-based detection systems, particularly support vector machines (SVMs), demonstrate high accuracy in their defense against conventional spoofing attacks. However, their robustness against intelligent adversaries remains largely unexplored. In this work, we reveal a critical vulnerability in an SVM-based GPS spoofing detection model by analyzing its decision boundary. Exploiting this weakness, we introduce novel evasion strategies that craft adversarial GPS signals to evade the SVM detector: a data location shift attack and a similarity-based noise attack, along with their combination. Extensive simulations in the CARLA environment demonstrate that a modest positional shift reduces detection accuracy from $99.9\%$ to $20.4\%$, whereas similarity to genuine GPS noise-driven perturbations remain largely undetected, while gradually degrading performance. A critical threshold reveals a nonlinear cancellation effect between similarity and shift, underscoring a fundamental detectability–impact trade-off. To our knowledge, these findings represent the first demonstration of such an evasion attack against SVM-based GPS spoofing defenses, suggesting a need to improve the adversarial robustness of machine-learning-based spoofing detection in vehicular systems. Full article

In today’s digital era, cyberattacks are rapidly evolving, rendering traditional security mechanisms increasingly inadequate. The adoption of AI-based Network Intrusion Detection Systems (NIDS) has emerged as a promising solution, due to their ability to detect and respond to malicious activity using machine learning techniques. However, these systems remain vulnerable to adversarial threats, particularly data poisoning attacks, in which attackers manipulate training data to degrade model performance. In this work, we examine tree classifiers, Random Forest and Gradient Boosting, to model black box poisoning attacks. We introduce FortiNIDS, a robust framework that employs a surrogate neural network to generate adversarial perturbations that can transfer between models, leveraging the transferability of adversarial examples. In addition, we investigate defense strategies designed to improve the resilience of NIDS in smart city Internet of Things (IoT) settings. Specifically, we evaluate adversarial training and the Reject on Negative Impact (RONI) technique using the widely adopted CICDDoS2019 dataset. Our findings highlight the effectiveness of targeted defenses in improving detection accuracy and maintaining system reliability under adversarial conditions, thereby contributing to the security and privacy of smart city networks. Full article

Motion capture (MoCap) data derived from wearable Inertial Measurement Units is essential to applications in sports science and healthcare robotics. However, a significant amount of the potential of this data is limited due to missing data derived from sensor limitations, network issues, and environmental interference. Such limitations can introduce bias, prevent the fusion of critical data streams, and ultimately compromise the integrity of human activity analysis. Despite the plethora of data imputation techniques available, there have been few systematic performance evaluations of these techniques explicitly for the time series data of IMU-derived MoCap data. We address this by evaluating the imputation performance across three distinct contexts: univariate time series, multivariate across players, and multivariate across kinematic angles. To address this limitation, we propose a systematic comparative analysis of imputation techniques, including statistical, machine learning, and deep learning techniques, in this paper. We also introduce the first publicly available MoCap dataset specifically for the purpose of benchmarking missing value imputation, with three missingness mechanisms: missing completely at random, block missingness, and a simulated value-dependent missingness pattern simulated at the signal transition points. Using data from 53 karate practitioners performing standardized movements, we artificially generated missing values to create controlled experimental conditions. We performed experiments across the 53 subjects with 39 kinematic variables, which showed that discriminating between univariate and multivariate imputation frameworks demonstrates that multivariate imputation frameworks surpassunivariate approaches when working with more complex missingness mechanisms. Specifically, multivariate approaches achieved up to a 50% error reduction (with the MAE improving from 10.8 ± 6.9 to 5.8 ± 5.5) compared to univariate methods for transition point missingness. Specialized time series deep learning models (i.e., SAITS, BRITS, GRU-D) demonstrated a superior performance with MAE values consistently below 8.0 for univariate contexts and below 3.2 for multivariate contexts across all missing data percentages, significantly surpassing traditional machine learning and statistical methods. Notable traditional methods such as Generative Adversarial Imputation Networks and Iterative Imputers exhibited a competitive performance but remained less stable than the specialized temporal models. This work offers an important baseline for future studies, in addition to recommendations for researchers looking to increase the accuracy and robustness of MoCap data analysis, as well as integrity and trustworthiness. Full article

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article

This paper presents a comparative study on the impact of robust hashing in enhancing image-based malware classification. While Convolutional Neural Networks (CNNs) have shown promise when working with image-based malware samples, their performance degrades significantly when obfuscation techniques are taken into consideration to hamper the malware classification or detection. To address this, we apply a robust hashing technique that generates invariant visual representations of malware samples, enabling improved generalization under obfuscation implemented as image salting. Using a custom obfuscation method to simulate polymorphic variants, we evaluate MobileNet, ResNet, and DenseNet architectures across five salting conditions (0% to 40%). The results demonstrate that robust hashing substantially boosts classification accuracy, with DenseNet achieving 89.50% on unsalted data, compared to only 68.00% without hashing. Across all salting levels, models consistently performed better when robust hashing was applied, confirming its effectiveness in preserving structural features and mitigating adversarial noise. These findings position robust hashing as a powerful preprocessing strategy for resilient malware detection. Full article

Adversarial attacks involve malicious actors introducing intentional perturbations to machine learning (ML) models, causing unintended behavior. This poses a significant threat to the integrity and trustworthiness of ML models, necessitating the development of robust detection techniques to protect systems from potential threats. The paper proposes a new approach for detecting adversarial attacks using a surrogate model and diagnostic attributes. The method was tested on 22 tabular datasets on which four different ML models were trained. Furthermore, various attacks were conducted, which led to obtaining perturbed data. The proposed approach is characterized by high efficiency in detecting known and unknown attacks—balanced accuracy was above 0.94, with very low false negative rates (0.02–0.10) for binary detection. Sensitivity analysis shows that classifiers trained based on diagnostic attributes can detect even very subtle adversarial attacks. Full article

Geospatial Artificial Intelligence (GeoAI) offers a scalable solution for automating the generation and updating of volunteered geographic information (VGI) maps—addressing the limitations of manual contributions to crowd-source mapping platforms such as OpenStreetMap (OSM). This study evaluates the accuracy of GeoAI-generated buildings specifically, using two Generative Adversarial Network (GAN) models. These are OSM-GAN—trained on OSM vector data and Google Earth imagery—and OSi-GAN—trained on authoritative “ground truth” Ordnance Survey Ireland (OSi) vector data and aerial orthophotos. Altogether, we assess map feature completeness, shape accuracy, and positional accuracy and conduct qualitative visual evaluations using live OSM database features and OSi map data as a benchmark. The results show that OSi-GAN achieves higher completeness (88.2%), while OSM-GAN provides more consistent shape fidelity (mean HD: 3.29 m; σ = 2.46 m) and positional accuracy (mean centroid distance: 1.02 m) compared to both OSi-GAN and the current OSM map. The OSM dataset exhibits moderate average deviation (mean HD 5.33 m) but high variability, revealing inconsistencies in crowd-source mapping. These empirical results demonstrate the potential of GeoAI to augment manual VGI mapping workflows to support timely downstream applications in urban planning, disaster response, and many other location-based services (LBSs). The findings also emphasize the need for robust Quality Assurance (QA) frameworks to address “AI slop” and ensure the reliability and consistency of GeoAI-generated data. Full article