Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly ...The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly detection.Accurate identification of anomalous patterns in building energy consumption is essential for optimizing operations,improving energy efficiency,and supporting grid reliability.This study investigates advanced feature engineering and machine learning modeling techniques for large-scale time series anomaly detection in building energy systems.Expanding upon previous benchmark frameworks,we introduce additional features such as oil price indices and solar cycle indicators,including sunset and sunrise times,to enhance the contextual understanding of consumption patterns.Our comparative modeling approach encompasses an extensive suite of algorithms,including KNeighborsUnif,KNeighborsDist,LightGBMXT,LightGBM,RandomForestMSE,CatBoost,ExtraTreesMSE,NeuralNetFastAI,XGBoost,NeuralNetTorch,and LightGBMLarge.Data preprocessing includes rigorous handling of missing values and normalization,while feature engineering focuses on temporal,environmental,and value-change attributes.The models are evaluated on a comprehensive dataset of smart meter readings,with performance assessed using metrics such as the Area Under the Receiver Operating Characteristic Curve(AUC-ROC).The results demonstrate that the integration of diverse exogenous variables and a hybrid ensemble of traditional tree-based and neural network models can significantly improve anomaly detection performance.This work provides new insights into the design of robust,scalable,and generalizable frameworks for energy anomaly detection in complex,real-world settings.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.Thi...The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.展开更多
Summer rainfall in the Yangtze River basin(YRB)is favored by two key factors in the lower troposphere:the tropical anticyclonic anomaly over the western North Pacific and the extratropical northeasterly anomalies to t...Summer rainfall in the Yangtze River basin(YRB)is favored by two key factors in the lower troposphere:the tropical anticyclonic anomaly over the western North Pacific and the extratropical northeasterly anomalies to the north of the YRB.This study,however,found that approximately 46%of heavy rainfall events in the YRB occur when only one factor appears and the other is opposite signed.Accordingly,these heavy rainfall events can be categorized into two types:the extratropical northeasterly anomalies but tropical cyclonic anomaly(first unconventional type),and the tropical anticyclonic anomaly but extratropical southwesterly anomalies(second unconventional type).Anomalous water vapor convergence and upward motion exists for both types,but through different mechanisms.For the first type,the moisture convergence and upward motion are induced by a cyclonic anomaly over the YRB,which appears in the mid and lower troposphere and originates from the upstream region.For the second type,a mid-tropospheric cyclonic anomaly over Lake Baikal extends southward and results in southwesterly anomalies over the YRB,in conjunction with the tropical anticyclonic anomaly.The southwesterly anomalies transport water vapor to the YRB and lead to upward motion through warm advection.This study emphasizes the role of mid-tropospheric circulations in inducing heavy rainfall in the YRB.展开更多
With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a ...With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.展开更多
Tooth developmental anomalies are a group of disorders caused by unfavorable factors affecting the tooth development process,resulting in abnormalities in tooth number,structure,and morphology.These anomalies typicall...Tooth developmental anomalies are a group of disorders caused by unfavorable factors affecting the tooth development process,resulting in abnormalities in tooth number,structure,and morphology.These anomalies typically manifest during childhood,impairing dental function,maxillofacial development,and facial aesthetics,while also potentially impacting overall physical and mental health.The complex etiology and diverse clinical phenotypes of these anomalies pose significant challenges for prevention,early diagnosis,and treatment.As they usually emerge early in life,long-term management and multidisciplinary collaboration in dental care are essential.However,there is currently a lack of systematic clinical guidelines for the diagnosis and treatment of these conditions,adding to the difficulties in clinical practice.In response to this need,this expert consensus summarizes the classifications,etiology,typical clinical manifestations,and diagnostic criteria of tooth developmental anomalies based on current clinical evidence.It also provides prevention strategies and stage-specific clinical management recommendations to guide clinicians in diagnosis and treatment,promoting early intervention and standardized care for these anomalies.展开更多
With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT termi...With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.展开更多
In the field of intelligent surveillance,weakly supervised video anomaly detection(WSVAD)has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels.Although mu...In the field of intelligent surveillance,weakly supervised video anomaly detection(WSVAD)has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels.Although multiple instance learning(MIL)has dominated the WSVAD for a long time,its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events.In addition,insufficient temporal modeling obscures causal relationships between events,making anomaly decisions reactive rather than reasoning-based.To overcome the limitations above,this paper proposes an adaptive knowledgebased guidance method that integrates external structured knowledge.The approach combines hierarchical category information with learnable prompt vectors.It then constructs continuously updated contextual references within the feature space,enabling fine-grained meaning-based guidance over video content.Building on this,the work introduces an event relation analysis module.This module explicitly models temporal dependencies and causal correlations between video snippets.It constructs an evolving logic chain of anomalous events,revealing the process by which isolated anomalous snippets develop into a complete event.Experiments on multiple benchmark datasets show that the proposed method achieves highly competitive performance,achieving an AUC of 88.19%on UCF-Crime and an AP of 86.49%on XD-Violence.More importantly,the method provides temporal and causal explanations derived from event relationships alongside its detection results.This capability significantly advances WSVAD from a simple binary classification to a new level of interpretable behavior analysis.展开更多
Basement structures may influence how ruptures propagate during an earthquake.However,most structural evidence is beneath the thick layer of younger volcanic sediments.In this study,gravity method was applied to disco...Basement structures may influence how ruptures propagate during an earthquake.However,most structural evidence is beneath the thick layer of younger volcanic sediments.In this study,gravity method was applied to discover more features of the basement structure.A land survey of gravity measurement was conducted at 383 stations south of Toba.The observed gravity was then used to generate Complete Bouguer Anomaly and residual-regional anomaly maps.In addition,several edge enhancements based on derivations were applied.All results presented lineations that could be linked to previously recognized active faults and structures.Additionally,the most prominent feature is a large northwest-southeast elongated high anomaly,almost sub-parallel to the Sumatra Fault Zone(SFZ).Since the feature is also located at the continuation of the Medial Sumatra Tectonic Zone(MSTZ),the body might be the hidden part of this major tectonic zone.The occurrence of MSTZ across the SFZ would affect the rupture propagation of earthquake events in the fault segment of the SFZ.展开更多
Using the data of 500 hPa geopotential height from 1951 to 1995, SST roughly in the same period and OLR data from 1974 to 1994, the relation between the anomalies of subtropical high (STH for short) and the tropical c...Using the data of 500 hPa geopotential height from 1951 to 1995, SST roughly in the same period and OLR data from 1974 to 1994, the relation between the anomalies of subtropical high (STH for short) and the tropical circulations including the Asian monsoon as well as the convective activity are studied. In order to study the physical process of the air-sea interaction related to STH anomaly, the correlation of STH with SST at various sea areas, lagged and simultaneous, has been calculated. Comparing the difference of OLR, wind fields, vertical circulations and SST anomalies in the strong and weak STH, we investigate the characteristics of global circulations and the SST distributions related to the anomalous STH at the western Pacific both in winter and summer. Much attention has been paid to the study of the air-sea interaction and the relationship between the East Asian monsoon and the STH in the western Pacific. A special vertical circulation, related to the STH anomalies is found, which connects the monsoon current to the west and the vertical flow influenced by the SST anomaly in the tropical eastern Pacific.展开更多
The microwave-induced thermoacoustic imaging(TAI)technology has both the advantages of high contrast of microwave imaging and high resolution of ultrasound imaging(UI),so it has carried out exploratory application res...The microwave-induced thermoacoustic imaging(TAI)technology has both the advantages of high contrast of microwave imaging and high resolution of ultrasound imaging(UI),so it has carried out exploratory application research in various areas,such as the early detection of breast tumors and cerebrovascular diseases.However,the microwave generator used in the traditional TAI technology is huge and expensive,and the temporal resolution is also too low due to the single-element scanning mechanism.Thus,it is difficult to meet the needs of clinical applications.In this paper,the iterative process and the analysis of related application scenarios from single-element scanning to portable and array-based TAI,such as the miniaturized microwave generator,handheld antenna,multi-channel data acquisition,and UI/TAIdual-modality imaging,are reviewed,and the future trends of this technology are discussed.This review helps researchers in the field of TAI learn the technological development process and future trends.It also deepens clinicians’understanding of TAI so as to put forward more application requirements.展开更多
Apart from usual quantization steps on the ballistic conductance of quasi-one-dimensional conductor, an additional plateau-like feature appears at a fraction of about 0.7 below the first conductance step in GaAs-based...Apart from usual quantization steps on the ballistic conductance of quasi-one-dimensional conductor, an additional plateau-like feature appears at a fraction of about 0.7 below the first conductance step in GaAs-based quantum point contacts (QPCs). Despite a tremendous amount of research on this anomalous feature, its origin remains still unclear. Here, a unique model of this anomaly is proposed relying on fundamental principles of quantum mechanics. It is noticed that just after opening a quasi-1D conducting channel in the QPC a single electron travels the channel at a time, and such electron can be—in principle—observed. The act of observation destroys superposition of spin states, in which the electron otherwise exists, and this suppresses their quantum interference. It is shown that then the QPC-conductance is reduced by a factor of 0.74. “Visibility” of electron is enhanced if the electron spends some time in the channel due to resonant transmission. Electron’s resonance can also explain an unusual temperature behavior of the anomaly as well as its recently discovered feature: oscillatory modulation as a function of the channel length and electrostatic potential. A recipe for experimental verification of the model is given.展开更多
In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause o...In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause of life-threating hemorrhage and the different causes of uterine pseudoaneurysms.Uterine artery pseudoaneurysm is a complication of both surgical gynecological and nontraumatic procedures.Massive hemorrhage is the consequence of the rupture of the pseudoaneurysm.Uterine artery pseudoaneurysm can develop after obstetric or gynecological procedures,being the most frequent after cesarean or vaginal deliveries,curettage and even during pregnancy.However,there are several cases described unrelated to pregnancy,such as after conization,hysteroscopic surgery or laparoscopic myomectomy.Hemorrhage is the clinical manifestation and it can be life-threatening so suspicion of this vascular lesion is essential for early diagnosis and treatment.However,there are other uterine vascular anomalies that may be the cause of severe hemorrhage,which must be taken into account in the differential diagnosis.Computed tomography angiography and embolization is supposed to be the first therapeutic option in most of them.展开更多
Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which ...Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.展开更多
Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract loc...Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features;Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection model based on parallel dilated convolution and residual learning (Res-PDC). To better explore the interactive relationships between features, the traffic samples are converted into two-dimensional matrix. A module combining parallel dilated convolutions and residual learning (res-pdc) was designed to extract local and global features of traffic at different scales. By utilizing res-pdc modules with different dilation rates, we can effectively capture spatial features at different scales and explore feature dependencies spanning wider regions without increasing computational resources. Secondly, to focus and integrate the information in different feature subspaces, further enhance and extract the interactions among the features, multi-head attention is added to Res-PDC, resulting in the final model: multi-head attention enhanced parallel dilated convolution and residual learning (MHA-Res-PDC) for network traffic anomaly detection. Finally, comparisons with other machine learning and deep learning algorithms are conducted on the NSL-KDD and CIC-IDS-2018 datasets. The experimental results demonstrate that the proposed method in this paper can effectively improve the detection performance.展开更多
The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charg...The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.展开更多
Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies a...Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies associated with adverse geology provide a novel strategy for addressing these limitations.However,statistical methods for identifying geochemical anomalies are insufficient for tunnel engineering.In contrast,data mining techniques such as machine learning have demonstrated greater efficacy when applied to geological data.Herein,a method for identifying adverse geology using machine learning of geochemical anomalies is proposed.The method was identified geochemical anomalies in tunnel that were not identified by statistical methods.We by employing robust factor analysis and self-organizing maps to reduce the dimensionality of geochemical data and extract the anomaly elements combination(AEC).Using the AEC sample data,we trained an isolation forest model to identify the multi-element anomalies,successfully.We analyzed the adverse geological features based the multi-element anomalies.This study,therefore,extends the traditional approach of geological analysis in tunnels and demonstrates that machine learning is an effective tool for intelligent geological analysis.Correspondingly,the research offers new insights regarding the adverse geology and the prevention of hazards during the construction of tunnels and underground engineering projects.展开更多
As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and...As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.展开更多
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
文摘The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly detection.Accurate identification of anomalous patterns in building energy consumption is essential for optimizing operations,improving energy efficiency,and supporting grid reliability.This study investigates advanced feature engineering and machine learning modeling techniques for large-scale time series anomaly detection in building energy systems.Expanding upon previous benchmark frameworks,we introduce additional features such as oil price indices and solar cycle indicators,including sunset and sunrise times,to enhance the contextual understanding of consumption patterns.Our comparative modeling approach encompasses an extensive suite of algorithms,including KNeighborsUnif,KNeighborsDist,LightGBMXT,LightGBM,RandomForestMSE,CatBoost,ExtraTreesMSE,NeuralNetFastAI,XGBoost,NeuralNetTorch,and LightGBMLarge.Data preprocessing includes rigorous handling of missing values and normalization,while feature engineering focuses on temporal,environmental,and value-change attributes.The models are evaluated on a comprehensive dataset of smart meter readings,with performance assessed using metrics such as the Area Under the Receiver Operating Characteristic Curve(AUC-ROC).The results demonstrate that the integration of diverse exogenous variables and a hybrid ensemble of traditional tree-based and neural network models can significantly improve anomaly detection performance.This work provides new insights into the design of robust,scalable,and generalizable frameworks for energy anomaly detection in complex,real-world settings.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01276).
文摘The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
基金supported by the National Natural Science Foundation of China(Grant No.42275041)the Hainan Province Science and Technology Special Fund(Grant No.SOLZSKY2025006).
文摘Summer rainfall in the Yangtze River basin(YRB)is favored by two key factors in the lower troposphere:the tropical anticyclonic anomaly over the western North Pacific and the extratropical northeasterly anomalies to the north of the YRB.This study,however,found that approximately 46%of heavy rainfall events in the YRB occur when only one factor appears and the other is opposite signed.Accordingly,these heavy rainfall events can be categorized into two types:the extratropical northeasterly anomalies but tropical cyclonic anomaly(first unconventional type),and the tropical anticyclonic anomaly but extratropical southwesterly anomalies(second unconventional type).Anomalous water vapor convergence and upward motion exists for both types,but through different mechanisms.For the first type,the moisture convergence and upward motion are induced by a cyclonic anomaly over the YRB,which appears in the mid and lower troposphere and originates from the upstream region.For the second type,a mid-tropospheric cyclonic anomaly over Lake Baikal extends southward and results in southwesterly anomalies over the YRB,in conjunction with the tropical anticyclonic anomaly.The southwesterly anomalies transport water vapor to the YRB and lead to upward motion through warm advection.This study emphasizes the role of mid-tropospheric circulations in inducing heavy rainfall in the YRB.
基金National Natural Science Foundation of China(Grant No.62103434)National Science Fund for Distinguished Young Scholars(Grant No.62176263).
文摘With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.
基金supported by the grants No.82370912 from the National Natural Science Foundation of ChinaNo.2022020801010499 from the Bureau of Science and Technology of Wuhan,ChinaNo.2042023kf0231 from the Fundamental Research Funds for the Central Universities,China。
文摘Tooth developmental anomalies are a group of disorders caused by unfavorable factors affecting the tooth development process,resulting in abnormalities in tooth number,structure,and morphology.These anomalies typically manifest during childhood,impairing dental function,maxillofacial development,and facial aesthetics,while also potentially impacting overall physical and mental health.The complex etiology and diverse clinical phenotypes of these anomalies pose significant challenges for prevention,early diagnosis,and treatment.As they usually emerge early in life,long-term management and multidisciplinary collaboration in dental care are essential.However,there is currently a lack of systematic clinical guidelines for the diagnosis and treatment of these conditions,adding to the difficulties in clinical practice.In response to this need,this expert consensus summarizes the classifications,etiology,typical clinical manifestations,and diagnostic criteria of tooth developmental anomalies based on current clinical evidence.It also provides prevention strategies and stage-specific clinical management recommendations to guide clinicians in diagnosis and treatment,promoting early intervention and standardized care for these anomalies.
基金supported by National Key R&D Program of China(No.2022YFB3105101).
文摘With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.
文摘In the field of intelligent surveillance,weakly supervised video anomaly detection(WSVAD)has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels.Although multiple instance learning(MIL)has dominated the WSVAD for a long time,its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events.In addition,insufficient temporal modeling obscures causal relationships between events,making anomaly decisions reactive rather than reasoning-based.To overcome the limitations above,this paper proposes an adaptive knowledgebased guidance method that integrates external structured knowledge.The approach combines hierarchical category information with learnable prompt vectors.It then constructs continuously updated contextual references within the feature space,enabling fine-grained meaning-based guidance over video content.Building on this,the work introduces an event relation analysis module.This module explicitly models temporal dependencies and causal correlations between video snippets.It constructs an evolving logic chain of anomalous events,revealing the process by which isolated anomalous snippets develop into a complete event.Experiments on multiple benchmark datasets show that the proposed method achieves highly competitive performance,achieving an AUC of 88.19%on UCF-Crime and an AP of 86.49%on XD-Violence.More importantly,the method provides temporal and causal explanations derived from event relationships alongside its detection results.This capability significantly advances WSVAD from a simple binary classification to a new level of interpretable behavior analysis.
基金National Research and Innovation Agency(BRIN),Indonesia,with Grant No.373/II/FR/3/2022(Expedition and Exploration Fund),676/III/PR.01.December 03,2021(Geological Hazard In-House Program)。
文摘Basement structures may influence how ruptures propagate during an earthquake.However,most structural evidence is beneath the thick layer of younger volcanic sediments.In this study,gravity method was applied to discover more features of the basement structure.A land survey of gravity measurement was conducted at 383 stations south of Toba.The observed gravity was then used to generate Complete Bouguer Anomaly and residual-regional anomaly maps.In addition,several edge enhancements based on derivations were applied.All results presented lineations that could be linked to previously recognized active faults and structures.Additionally,the most prominent feature is a large northwest-southeast elongated high anomaly,almost sub-parallel to the Sumatra Fault Zone(SFZ).Since the feature is also located at the continuation of the Medial Sumatra Tectonic Zone(MSTZ),the body might be the hidden part of this major tectonic zone.The occurrence of MSTZ across the SFZ would affect the rupture propagation of earthquake events in the fault segment of the SFZ.
文摘Using the data of 500 hPa geopotential height from 1951 to 1995, SST roughly in the same period and OLR data from 1974 to 1994, the relation between the anomalies of subtropical high (STH for short) and the tropical circulations including the Asian monsoon as well as the convective activity are studied. In order to study the physical process of the air-sea interaction related to STH anomaly, the correlation of STH with SST at various sea areas, lagged and simultaneous, has been calculated. Comparing the difference of OLR, wind fields, vertical circulations and SST anomalies in the strong and weak STH, we investigate the characteristics of global circulations and the SST distributions related to the anomalous STH at the western Pacific both in winter and summer. Much attention has been paid to the study of the air-sea interaction and the relationship between the East Asian monsoon and the STH in the western Pacific. A special vertical circulation, related to the STH anomalies is found, which connects the monsoon current to the west and the vertical flow influenced by the SST anomaly in the tropical eastern Pacific.
基金supported in part by the National Key Research and Development Program of China under Grant No.2018YFB1801503National Natural Science Foundation of China under Grants No.61931006,No.82071940,No.62101111,No.U20A20212,No.61921002,and No.U1930127+1 种基金Fundamental Research Funds for the Central Universities under Grants No.ZYGX2020ZB011 and No.ZYGX2019J013Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China under Grants No.ZYGX2021YGLH205 and No.ZYGX2021YGLH216.
文摘The microwave-induced thermoacoustic imaging(TAI)technology has both the advantages of high contrast of microwave imaging and high resolution of ultrasound imaging(UI),so it has carried out exploratory application research in various areas,such as the early detection of breast tumors and cerebrovascular diseases.However,the microwave generator used in the traditional TAI technology is huge and expensive,and the temporal resolution is also too low due to the single-element scanning mechanism.Thus,it is difficult to meet the needs of clinical applications.In this paper,the iterative process and the analysis of related application scenarios from single-element scanning to portable and array-based TAI,such as the miniaturized microwave generator,handheld antenna,multi-channel data acquisition,and UI/TAIdual-modality imaging,are reviewed,and the future trends of this technology are discussed.This review helps researchers in the field of TAI learn the technological development process and future trends.It also deepens clinicians’understanding of TAI so as to put forward more application requirements.
文摘Apart from usual quantization steps on the ballistic conductance of quasi-one-dimensional conductor, an additional plateau-like feature appears at a fraction of about 0.7 below the first conductance step in GaAs-based quantum point contacts (QPCs). Despite a tremendous amount of research on this anomalous feature, its origin remains still unclear. Here, a unique model of this anomaly is proposed relying on fundamental principles of quantum mechanics. It is noticed that just after opening a quasi-1D conducting channel in the QPC a single electron travels the channel at a time, and such electron can be—in principle—observed. The act of observation destroys superposition of spin states, in which the electron otherwise exists, and this suppresses their quantum interference. It is shown that then the QPC-conductance is reduced by a factor of 0.74. “Visibility” of electron is enhanced if the electron spends some time in the channel due to resonant transmission. Electron’s resonance can also explain an unusual temperature behavior of the anomaly as well as its recently discovered feature: oscillatory modulation as a function of the channel length and electrostatic potential. A recipe for experimental verification of the model is given.
文摘In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause of life-threating hemorrhage and the different causes of uterine pseudoaneurysms.Uterine artery pseudoaneurysm is a complication of both surgical gynecological and nontraumatic procedures.Massive hemorrhage is the consequence of the rupture of the pseudoaneurysm.Uterine artery pseudoaneurysm can develop after obstetric or gynecological procedures,being the most frequent after cesarean or vaginal deliveries,curettage and even during pregnancy.However,there are several cases described unrelated to pregnancy,such as after conization,hysteroscopic surgery or laparoscopic myomectomy.Hemorrhage is the clinical manifestation and it can be life-threatening so suspicion of this vascular lesion is essential for early diagnosis and treatment.However,there are other uterine vascular anomalies that may be the cause of severe hemorrhage,which must be taken into account in the differential diagnosis.Computed tomography angiography and embolization is supposed to be the first therapeutic option in most of them.
基金supported by the National Natural Science Foundation of China(No.52188102).
文摘Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.
基金supported by the Xiamen Science and Technology Subsidy Project(No.2023CXY0318).
文摘Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features;Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection model based on parallel dilated convolution and residual learning (Res-PDC). To better explore the interactive relationships between features, the traffic samples are converted into two-dimensional matrix. A module combining parallel dilated convolutions and residual learning (res-pdc) was designed to extract local and global features of traffic at different scales. By utilizing res-pdc modules with different dilation rates, we can effectively capture spatial features at different scales and explore feature dependencies spanning wider regions without increasing computational resources. Secondly, to focus and integrate the information in different feature subspaces, further enhance and extract the interactions among the features, multi-head attention is added to Res-PDC, resulting in the final model: multi-head attention enhanced parallel dilated convolution and residual learning (MHA-Res-PDC) for network traffic anomaly detection. Finally, comparisons with other machine learning and deep learning algorithms are conducted on the NSL-KDD and CIC-IDS-2018 datasets. The experimental results demonstrate that the proposed method in this paper can effectively improve the detection performance.
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
基金the support from the National Natural Science Foundation of China(Nos.52279103,52379103)the Natural Science Foundation of Shandong Province(No.ZR2023YQ049)。
文摘Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies associated with adverse geology provide a novel strategy for addressing these limitations.However,statistical methods for identifying geochemical anomalies are insufficient for tunnel engineering.In contrast,data mining techniques such as machine learning have demonstrated greater efficacy when applied to geological data.Herein,a method for identifying adverse geology using machine learning of geochemical anomalies is proposed.The method was identified geochemical anomalies in tunnel that were not identified by statistical methods.We by employing robust factor analysis and self-organizing maps to reduce the dimensionality of geochemical data and extract the anomaly elements combination(AEC).Using the AEC sample data,we trained an isolation forest model to identify the multi-element anomalies,successfully.We analyzed the adverse geological features based the multi-element anomalies.This study,therefore,extends the traditional approach of geological analysis in tunnels and demonstrates that machine learning is an effective tool for intelligent geological analysis.Correspondingly,the research offers new insights regarding the adverse geology and the prevention of hazards during the construction of tunnels and underground engineering projects.
基金supported in part by the Guangxi Science and Technology Major Program under grant AA22068067the Guangxi Natural Science Foundation under grant 2023GXNSFAA026236 and 2024GXNSFDA010064the National Natural Science Foundation of China under project 62172119.
文摘As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.
