Search Results (179)

As distributed terminals are increasingly integrated into modern power systems with high penetration of renewable energy and decentralized resources, access control mechanisms must support continuous and highly detailed trust assessment. Existing approaches based on machine learning primarily rely on network traffic features from a single source and analyze terminals in isolation, which limits their ability to capture complex device states and correlated attack behaviors. This paper presents a trust assessment framework for distributed power grid terminals that combines multidimensional behavioral modeling with dual domain graph neural networks. Behavioral features are collected from network traffic, runtime environment, and hardware or kernel events and are fused into compact representations through a variational autoencoder to mitigate redundancy and reduce computational overhead. Based on the fused features and observed communication relationships, two graphs are constructed in parallel: a feature domain graph reflecting behavioral similarity and a topological domain graph capturing communication structure between terminals. Graph convolution is performed in both domains to jointly model individual behavioral risk and correlation across terminals. A fusion mechanism based on attention is further introduced to adaptively integrate embeddings specific to each domain, together with a loss function that enforces both shared and complementary representations across domains. Experiments conducted on the CIC EV Charger Attack Dataset 2024 show that the proposed framework achieves a classification accuracy of 96.84%, while maintaining a recall rate above 95% for the low trust category. These results indicate that incorporating multidimensional behavior perception and dual domain relational modeling improves trust assessment performance for distributed power grid terminals under complex attack scenarios. Full article

Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates this neurodynamic process to improve decoding and provide computational insights. This paper proposes a Structure-Feature Evolution Graph Attention Network (SFE-GAT). Its inter-layer evolution mechanism dynamically co-adapts graph topology and node features, mimicking functional network reorganization. Initialized with phase-locking value connectivity and spectral features, the model uses a graph autoencoder with Monte Carlo sampling to iteratively refine edges and embeddings. On the BCI Competition IV-2a dataset, SFE-GAT achieved 77.70% (subject-dependent) and 66.59% (subject-independent) accuracy, outperforming baselines. Evolved graphs showed sparsification and strengthening of task-critical connections, indicating hierarchical processing. This paper advances EEG decoding through a dynamic graph architecture, providing a computational framework for studying the hierarchical organization of motor cortex activity and linking adaptive graph learning with neural dynamics. Full article

As smart grids evolve into complex cyber-physical systems, conventional static defenses struggle to address time-varying topologies and Advanced Persistent Threats (APTs). We propose the Security Framework for Resilient Smart Grids based on Self-Organizing Graph Neural Cellular Automata (SG-GNC). Specifically, a Neural Homeostatic Embedding (NHE) mechanism utilizes variational graph autoencoders to construct a continuous health manifold for unsupervised anomaly detection, while a Neural Cellular Automata (NCA) engine employs shared-weight local rules to empower nodes with decentralized self-healing capabilities. Finally, a Generative Adversarial Immunity (GAI) strategy facilitates active defense co-evolution, enhancing robustness against zero-day attacks. Experimental results on the IEEE 118 and 300-bus systems demonstrate an average detection accuracy of 98.23%, significantly outperforming benchmarks. In scenarios involving dynamic topology and zero-day attacks, the framework maintains over 96% accuracy with an inference latency of only 9.45 ms. These findings validate the capability of SG-GNC to provide resilient, endogenous defense in complex heterogeneous environments. Full article

With the advancement of intelligent manufacturing strategies, Cyber–Physical Production Systems (CPPSs) generate massive amounts of multidimensional, dynamic, and non-stationary data, posing significant challenges to real-time Process Monitoring. Existing anomaly detection methods often suffer from insufficient feature robustness when dealing with complex spatio-temporal dynamics, high computational complexity, and difficulties in effectively capturing incipient faults within deep topological structures. To address these issues, this paper proposes a Spatio-Temporal Variational Graph Statistical Attention Autoencoder (ST-VGSAE). First, the framework performs end-to-end multi-scale temporal decomposition via an Adaptive Lifting Wavelet Module, which enhances feature robustness while effectively suppressing noise. Furthermore, a spatio-temporal Token statistical self-attention mechanism with linear complexity is incorporated. By modulating local features via global statistics, it significantly reduces computational costs while enhancing anomaly discriminability. Experiments on the Tennessee Eastman (TE) process dataset demonstrate that the proposed model significantly outperforms state-of-the-art methods in key metrics such as the Fault Detection Rate and the False Alarm Rate, exhibiting superior noise robustness and real-time performance. Full article

Identifying therapeutic target genes and their corresponding targeted drugs is of significant importance for the treatment of non-small cell lung cancer (NSCLC). This study proposes a multi-view graph auto-encoder model (MVGAE), which, together with the network-informed adaptive positive-unlabeled (NIAPU) and synthetic lethality multi-view graph auto-encoder (SLMGAE) model, constitutes an integrated computational framework. The framework integrates multi-source biological network data, including protein–protein interaction networks, disease-gene association information, and gene-drug bipartite graphs, for data mining. Through systematic analysis and computational screening, we ultimately predicted seven potential driver genes associated with NSCLC using the NIAPU model. The SLMGAE model predicted nine genes with synthetic lethality (SL) interactions to these driver genes as candidate therapeutic targets. Based on these SL targets, the MVGAE model further predicted corresponding targeted drugs. Notably, among the prioritized targets, existing studies indicate that ATR and RAD51 exhibit conditional SL effects in the context of functional impairment. Furthermore, several of the predicted candidate drugs (such as PAZOPANIB) have been previously reported to play a positive role in NSCLC treatment. This study highlights MVGAE as a novel computational framework for drug repurposing and demonstrates how its integration with complementary models can effectively prioritize potential therapeutic targets and candidate drugs, providing a robust computational basis for precision treatment strategies. Full article

In the field of ocean observation, we often face the challenge of the contradiction between the vast ocean environment and limited ocean sensor observations, resulting in significant sparsity in the acquired ocean sensor data. This sparse ocean sensor data typically exhibits characteristics such as discrete spatial distribution, discontinuous observation time, and vertical stratification with water depth variations. Current methods primarily employ rule-based quality control, time series modeling, or traditional graph neural networks for processing. This paper addresses the characteristics of sparse ocean sensor data, building upon these methods by further utilizing topological correlation and hierarchical feature modeling on a topological basis. It proposes a depth-aware heterogeneous spatiotemporal graph neural network (DAHSGNN) to achieve efficient anomaly detection and data correction for this type of data. DAHSGNN integrates discrete observation data along the depth axis using a local graph construction method. It employs hierarchical feature engineering to characterize the vertical stratification of the ocean. A Gaussian Hidden Markov Model is used to segment the water layers, and intra- and inter-layer trend features are extracted using a water layer probability-guided Transformer encoder. Then, a bidirectional long short-term memory deep sequence encoder captures the local dynamic context, thereby achieving fine-grained modeling of the ocean’s vertical stratification features. Finally, a heterogeneous graph autoencoder is used to reconstruct the site-level data distribution. Experiments were conducted using multiple environmental variables from the International Seabed Authority (ISA) DeepData database. Results show that DAHSGNN exhibits good cross-variable generalization ability, achieves higher reconstruction accuracy than baseline methods, and significantly improves anomaly detection performance. Full article

Regional epidemic risk prediction requires holistic modeling of heterogeneous data sources such as demographic structure, health capacity, geographical features and human mobility. In this study, a unique and multi-modal epidemiological data set integrating demographic, health, geographic and mobility indicators of Türkiye and its neighboring countries was collected. Türkiye’s neighboring countries are Greece, Bulgaria, Georgia, Armenia, Iran, and Iraq. This dataset, created by combining raw data from these neighboring countries, provides a comprehensive regional representation that allows for both quantitative classification and spatial mapping of epidemiological risk. To address the class imbalance problem, Conditional GAN (CGAN), a class-conditional synthetic example generation approach that enhances high-risk category representation was used. In this study, we proposed a multi-view deep learning model named MV-RiskNet, which effectively models the multi-dimensional data structure by processing each view into independent subnetworks and integrating the representations with an attention-based fusion mechanism for regional epidemic risk prediction. Experimental studies were compared using Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Autoencoder classifier, and Graph Convolutional Network (GCN) models. The proposed MV-RiskNet with CGAN model achieved better results compared to other models, with 97.22% accuracy and 97.40% F1-score. The generated risk maps reveal regional clustering patterns in a spatially consistent manner, while attention analyses show that demographic and geographic features are the dominant determinants, while mobility plays a complementary role, especially in high-risk regions. Full article

Runoff forecasting is an essential application in the management of water resources and sustainable development. In practice, there are limitations in the forecast results because of factors such as data unavailability, noise interference, and spatiotemporal variation in multi-site data. To overcome the limitations, this paper proposes a hybrid forecast model based on Autoencoder (AE), Sparsified Dynamic Graph Convolution (SDGC), and Autoformer. The AE cleans noise and sharpens feature representation, the SDGC constructs dynamic adjacency matrices via the Multidimensional Dynamic Time Warping (MDTW) and sparsifies with a parameterized Multi-Layer Perceptron (MLP) to capture time-varying spatial correlations among stations, and the Autoformer decomposes features to model long-term nonlinear runoff trends through its autocorrelation mechanism. The experiment was carried out in six locations in the southeastern part of Guizhou province during the wet and dry periods and was contrasted with different mainstream models and supplemented with hydrological mechanism consistency analysis. Experimental results show that the hybrid model performs better than all the other models. In the short-term runoff simulation at XingHua Station during the wet season, NSE attains the maximum value of 0.891, with RMSE decreased by 6.5% to 24.1% and MAE by 20.2% to 35.5%. This model provides accurate runoff data to support flood early warning, dry-season water scheduling, and ecological flow protection, offering a reliable tool for sustainable water resource management in complex karst basins. Full article

Antimicrobial resistance represents one of the most critical global health challenges of the 21st century, urgently demanding innovative strategies for antimicrobial discovery. Traditional antibiotic development pipelines are slow, costly, and increasingly ineffective against multidrug-resistant pathogens. In this context, recent advances in artificial intelligence have emerged as transformative tools capable of accelerating antimicrobial discovery and expanding accessible chemical and biological space. This comprehensive review critically synthesizes recent progress in AI-driven approaches applied to the discovery and design of both small-molecule antibiotics and antimicrobial peptides. We examine how machine learning, deep learning, and generative models are being leveraged for virtual screening, activity prediction, mechanism-informed prioritization, and de novo antimicrobial design. Particular emphasis is placed on graph-based neural networks, attention-based and transformer architectures, and generative frameworks such as variational autoencoders and large language model-based generators. Across these approaches, AI has enabled the identification of structurally novel compounds, facilitated narrow-spectrum antimicrobial strategies, and improved interpretability in peptide prediction. However, significant challenges remain, including data scarcity and imbalance, limited experimental validation, and barriers to clinical translation. By integrating methodological advances with a critical analysis of the current limitations, this review highlights emerging trends and outlines future directions aimed at bridging the gap between in silico discovery and real-world therapeutic development. Full article

Wireless Sensor Networks (WSNs) are widely deployed for long-term monitoring in environments characterized by nonstationary sensing dynamics, intermittent connectivity and continuously evolving network topologies, while reliable, fine-grained labeled data capturing faults and adversarial behaviors remain scarce. This survey systematically reviews and synthesizes recent research that integrates autoencoder-based representation learning with self-supervised learning (SSL) objectives to enhance anomaly detection under these practical constraints. We structure the existing literature through a unified taxonomy encompassing autoencoder variants, self-supervised pretext tasks, spatio-temporal encoding mechanisms and the increasing use of graph-structured autoencoders for topology-aware modeling. Across distinct methodological categories, SSL-augmented frameworks consistently demonstrate improved robustness and stability compared to purely reconstruction-driven baselines, particularly in heterogeneous, dynamic and temporally drifting WSN environments. Nevertheless, this review also highlights several unresolved challenges that hinder real-world adoption, including uncertain scalability to large-scale networks, limited model interpretability, nontrivial energy and memory overheads on resource-constrained sensor nodes and a lack of standardized evaluation protocols and reporting practices. By consolidating publicly available datasets, experimental configurations and comparative performance trends, we derive concrete design requirements for robust and resource-aware anomaly detection in operational WSNs and outline promising future research directions, emphasizing lightweight model architectures, explainable learning mechanisms and federated AE–SSL paradigms to enable adaptive, privacy-preserving monitoring in next-generation IoT sensing systems. Full article

Overshooting defects in shipbuilding subassemblies are essential to ensure the final product’s overall integrity and safety. In this work, we focus on the automatic detection of overshooting defects in simple and T-shaped sub-assemblies by employing reconstruction-based unsupervised learning on 3D point clouds. To this purpose, we implemented and compared four state-of-the-art architectures, including a Variational Autoencoder (VAE), FoldingNet, a Dynamic Graph CNN (DGCNN) autoencoder, and a PointNet++ autoencoder. These architectures were trained exclusively on defect-free samples, anticipating the possibility of overshooting defects occurring in different locations and with varying geometric patterns that are difficult to characterize explicitly in advance. Those defects are then identified by applying an Isolation Forest to the reconstruction error features, enabling fully unsupervised anomaly detection and allowing us to study how the detection performance changes with the contamination parameter. The results show that reconstruction-based anomaly detection on point clouds is a viable strategy for identifying defects in an industrial environment and the importance of choosing architectures that balance detection performance, stability across different geometries, and computational cost. Full article

Battery energy storage systems (BESSs) play a vital role in contemporary smart grids, but their increasing digitalisation exposes them to sophisticated cyberattacks. Existing anomaly detection approaches typically treat sensor measurements as flat feature vectors, overlooking the intrinsic relational structure of cyber–physical systems. This work introduces an enhanced Graph Neural Network (GNN) autoencoder for unsupervised BESS anomaly detection that integrates multiscale graph construction, multi-head graph attention, manifold regularisation via latent compactness and graph smoothness, contrastive embedding shaping, and an ensemble anomaly scoring mechanism. A comprehensive evaluation across seven BESS and firmware cyberattack datasets demonstrates that the proposed method achieves near-perfect Receiver Operating Characteristic (ROC) and Precision–Recall Area Under the Curve (PR AUC) (up to 1.00 on several datasets), outperforming classical one-class models such as Isolation Forest, One-Class Support Vector Machine (One-Class SVM), and Local Outlier Factor on the most challenging scenarios. These results illustrate the strong potential of graph-informed representation learning for cybersecurity monitoring in distributed energy resource infrastructures. Full article

Cognitive diagnosis serves as a key component in personalized intelligent education, designed to accurately evaluate students’ knowledge states by analyzing their historical response data. It offers fundamental support for various educational applications such as adaptive learning and exercise recommendation. However, when leveraging student data, existing diagnostic models often incorporate sensitive attributes like family economic background and geographic location, which may lead to bias and unfairness. To address this issue, this paper introduces a Fairness-Aware Cognitive Diagnosis model (FACD) based on counterfactual graph representation learning. The approach builds student-centered causal subgraphs and integrates a graph variational autoencoder with adversarial learning to mitigate the influence of sensitive attributes on node representations. It further employs both central-node and neighbor-node perturbation strategies to generate counterfactual samples. A Siamese network is utilized to enforce representation consistency across different counterfactual scenarios, thereby deriving fair student contextual embeddings. Experimental results on the PISA 2015 dataset show that FACD outperforms conventional cognitive diagnosis models and their fairness-aware variants in terms of ACC, AUC, and RMSE. Ablation studies confirm the effectiveness and synergistic nature of each module. This work provides a viable pathway toward more reliable and equitable cognitive diagnosis systems. Full article

Satellite-aided multi-unmanned aerial vehicle (UAV) collaborative localization systems combine the extensive coverage of satellites with the flexibility of UAVs, offering new opportunities for locating highly dynamic emitters across large areas. However, the openness of space-air communication links and the increasing complexity of cybersecurity threats make these systems vulnerable to false data injection attacks. Most existing detection approaches focus only on temporal dependencies in time-frequency features and lack diagnostic mechanisms for identifying malicious UAVs, which limits their ability to effectively detect and mitigate such attacks. To address this issue, this paper proposes an intelligent collaborative localization framework that safeguards localization integrity by identifying and correcting false ranging information from malicious UAVs. The framework captures spatio-temporal correlations in multidimensional ranging sequences through a graph attention network (GAT) coupled with a time-attention-based variational autoencoder (VAE) to detect anomalies through anomalous distribution patterns. Malicious UAVs are further diagnosed through an anomaly scoring mechanism based on statistical analysis and reconstruction errors, while detected anomalies are corrected via a K-nearest neighbor-based (KNN) algorithm to enhance localization performance. Simulation results show that the proposed model improves localization accuracy by 25.9%, demonstrating the effectiveness of spatial–temporal feature extraction in securing collaborative localization. Full article

Root-cause analysis (RCA) in large-scale microservice-based payment systems is challenging due to complex failure propagation along service dependencies, limited availability of labeled incident data, and heterogeneous service topologies across deployments. We propose ServiceGraph-FM, a pretrained graph-based model for RCA, where “foundation” denotes a self-supervised graph encoder pretrained on large-scale production cluster traces and then adapted to downstream diagnosis. ServiceGraph-FM introduces three components: (1) masked graph autoencoding pretraining to learn transferable service-dependency embeddings for cross-topology generalization; (2) a temporal relational diffusion module that models anomaly propagation as graph diffusion on dynamic service graphs (i.e., Laplacian-governed information flow with learnable edge propagation strengths); and (3) a causal attention mechanism that leverages multi-hop path signals to better separate likely causes from correlated downstream effects. Experiments on the Alibaba Cluster Trace and synthetic PayPal-style topologies show that ServiceGraph-FM outperforms state-of-the-art baselines, improving Top-1 accuracy by 23.7% and Top-3 accuracy by 18.4% on average, and reducing mean time to detection by 31.2%. In zero-shot deployment on unseen architectures, the pretrained model retains 78.3% of its fully fine-tuned performance, indicating strong transferability for practical incident management. Full article