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32nd European Signal Processing Conference (EUSIPCO)

作者： Leon-Lopez, Kareth Fabre, Serge Manzoni, Fabio Mirambell, Laurent Tourneret, Jean-Yves Univ Toulouse IRIT ENSEEIHT TeSA Toulouse France TeSA Lab Toulouse France Hensoldt Nexeya France Toulouse France

ISBN: (纸本)9789464593617;9798331519773

The monitoring of abnormal ship behavior is an important task for maritime surveillance for which the automatic identification system (AIS) has been widely exploited. Several works have proposed graph-based anomaly detection (AD) methods on spatial AIS points to provide further information regarding the interactions between the observed data through graph structures. This paper studies a new AD framework on graphs constructed from AIS trajectories. This framework considers a diffusion kernel at multiple scales of the graph Laplacian matrix, referred to as multiscale AD for AIS trajectories (MAD-AIS). MAD-AIS builds an attributed graph from a set of AIS trajectories, where nodes encode spatio-temporal trajectories and edges connect them via a similarity measure. In a second stage, AD is performed by computing scaled versions of the graph Laplacian matrix that are used to assess the graph connectivity. Simulation results are first conducted on synthetic data with controlled ground truth showing that the proposed MAD-AIS can effectively detect the abnormal behavior of ships in terms of spatio-temporal irregularities. Simulations conducted on real AIS subtrajectories (i.e., segments of AIS trajectories) show that abnormal features/attributes can be localized along AIS paths.

关键词： graph anomaly detection multiscale diffusion graph automatic identification system maritime surveillance

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OCGATL: One-Class graph Attention Networks with Transformation Learning for anomaly detection for Argo Data 5th

OCGATL: One-Class Graph Attention Networks with Transformati...

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5th China Conference on Spatial Data and Intelligence (SpatialDI)

作者： Jiang, Yongguo Liu, Hua Wang, Jiaxing Zhai, Guangda Ocean Univ China Fac Informat Sci & Engn Qingdao 266000 Shandong Peoples R China

ISBN: (纸本)9789819729654;9789819729661

As the typical representative of marine big data, the Argo plan conducts high-quality and scientific anomaly detection on Argo data, which is an important step in ocean science big data. However, in classical anomaly algorithms, Argo anomaly detection mostly has low accuracy, poor efficiency, and neglects the spatial continuity of Argo data. In the research on anomaly detection of spatial and regional data, graph anomaly detection has achieved excellent results. In the research of graph anomaly detection, depth based classification as a downstream anomaly detection method performs well, but at the same time, there are also problems of hyper sphere collapse and performance flipping. This article focuses on the research work related to the above issues: (1) Based on the study of Argo data and graph data, combined with the three-dimensional spatial characteristics of Argo buoy data, a novel graph data construction method is proposed. (2) Propose to incorporate neural transformation learning into the architecture, improve data learning expression ability, and further improve the shortcomings of graph neural classification, enabling it to adapt to the spatiotemporal and multi-dimensional characteristics of Argo buoy data for outlier detection. This article conducts experiments on five simulation datasets to demonstrate that the improved idea outperforms five state-of-the-art graph anomaly detection algorithms in various indicators, successfully improving the problems of hyper sphere collapse and performance flipping, and enhancing the accuracy and robustness of graph anomaly detection;The effectiveness of graph construction was demonstrated by comparing it with classical anomaly algorithms on real Argo sample data.

关键词： Marine Data graph anomaly detection Hypersphere Learning Transformation learning

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graph Local Homophily Network for anomaly detection 24

Graph Local Homophily Network for Anomaly Detection

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Guo, Ronghui Zou, Minghui Zhang, Sai Zhang, Xiaowang Yu, Zhizhi Feng, Zhiyong Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9798400704369

In graph anomaly detection (GAD), the fact that anomalous nodes usually exhibit high heterophily, while most graph Neural Networks (GNNs) have homophily assumptions, leads to poor performance. Many studies have attempted to solve this problem by employing a set of graph filters covering various frequencies. Their ultimate goal is to design the most appropriate spectral filter to capture the complex signals generated by normals and anomalies. The critical aspect lies in the fusion of information from filters with different frequency response functions. However, existing methods lack a clear indicator to guide the fusion of information at different frequencies. In this paper, we find that local homophily is a valuable metric for assessing the weights of high- and low-frequency information at the node level, and explicitly point out that the accuracy of local homophily is positively correlated with the accuracy of anomaly detection. Moreover, we unveil the phenomenon of camouflage in anomalous, wherein these nodes disguise themselves by making their features resemble those of surrounding normals. Based on this investigation, we propose the graph Local Homophily Network for anomaly detection (GLHAD). Specifically, we first identify the local homophily of the nodes in the graph under the supervision of the labeled nodes, where two contrasting paradigms are employed to resist the camouflage of anomalies. Then, the local homophily-based combination module combines low- and high-frequency signals based on the predicted local homophily. Eventually, the node representations of different layers are aggregated to make finally predictions. Comprehensive experiments on four anomaly detection datasets show that GLHAD outperforms other state-of-the-art baselines.

关键词： graph anomaly detection Homophily Heterophily graph Neural Network

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Multi-representations Space Separation based graph-level anomaly-aware detection 23

Multi-representations Space Separation based Graph-level Ano...

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35th International Conference on Scientific and Statistical Database Management, (SSDBM)

作者： Lin, Fu Gong, Haonan Li, Mingkang Wang, Zitong Zhang, Yue Luo, Xuexiong Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Macquarie Univ Sch Comp Sydney NSW Australia

ISBN: (纸本)9798400707469

graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem. The objective of this research is centered on the particular issue that how to detect abnormal graphs within a graph set. The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies. Furthermore, abnormal graphs that have subtle differences from normal graphs are easily escaped detection by the existing methods. Thus, we propose a multi-representations space separation based graph-level anomaly-aware detection framework in this paper. To consider the different importance of node-level and graph-level anomalies, we design an anomaly-aware module to learn the specific-weight between them in the abnormal graph evaluation process. In addition, we learn strictly separate normal and abnormal graph representation spaces by four types of weighted graph representations against each other including anchor normal graphs, anchor abnormal graphs, training normal graphs, and training abnormal graphs. Based on the distance error between the graph representations of the test graph and both normal and abnormal graph representation spaces, we can accurately determine whether the test graph is anomalous. Our approach has been extensively evaluated against baseline methods using ten public graph datasets, and the results demonstrate its effectiveness. The code for our method is publicly available on https://***/whb605/***

关键词： graph anomaly detection graph neural networks graph representation learning

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anomaly detection with dual-channel heterogeneous graph based on hypersphere learning

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INFORMATION SCIENCES 2024年 681卷

作者： Li, Qing Wu, Guanzhong Ni, Hang You, Tao Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

graph anomaly detection is essential for identifying irregular patterns and outliers within complex network structures in domains like social networks, cybersecurity, finance, and transportation systems. It helps detect security breaches, fraud, and errors, improving decision-making and system reliability. Although current methodologies have advanced the unsupervised detection of graph anomalies, they frequently fail to fully address the nuanced specificities of graph anomalies, such as anomalous nodes, edges, and subgraphs. To overcome this limitation, the study presents MulDualGNN, a unique dual-channel heterogeneous graph anomaly detection framework. MulDualGNN incorporates a global substructure-aware GNN and a local substructure- aware GNN to capture both global and local substructure properties for accurate anomaly detection. Our model incorporates a multi-hypersphere learning target function, which includes macroscopic and mesoscopic hyperspheres. These hyperspheres measure abnormal nodes that deviate from most normal nodes in the entire graph and community structure, respectively. To overcome the model collapse problem in multi-hypersphere learning, our model utilizes the EmbSim similarity function to optimize the training target. The effectiveness and performance advantages of the proposed method are evaluated through extensive experiments on five datasets. The results demonstrate the superior performance of our approach in graph anomaly detection tasks.

关键词： graph anomaly detection graph neural network Hypersphere learning Dual-channel graph neural network Unsupervised learning

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One-class graph neural networks for anomaly detection in attributed networks

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NEURAL COMPUTING & APPLICATIONS 2021年第18期33卷 12073-12085页

作者： Wang, Xuhong Jin, Baihong Du, Ying Cui, Ping Tan, Yingshui Yang, Yupu Shanghai Jiao Tong Univ Minist Educ Key Lab Syst Control & Informat Proc Sch Elect Informat & Elect Engn Shanghai Peoples R China Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Nowadays, graph-structured data are increasingly used to model complex systems. Meanwhile, detecting anomalies from graph has become a vital research problem of pressing societal concerns. anomaly detection is an unsupervised learning task of identifying rare data that differ from the majority. As one of the dominant anomaly detection algorithms, one-class support vector machine has been widely used to detect outliers. However, those traditional anomaly detection methods lost their effectiveness in graph data. Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data. In this work, we propose one-class graph neural network (OCGNN), a one-class classification framework for graph anomaly detection. OCGNN is designed to combine the powerful representation ability of graph neural networks along with the classical one-class objective. Compared with other baselines, OCGNN achieves significant improvements in extensive experiments.

关键词： graph anomaly detection graph neural network anomaly detection Unsupervised learning

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graph Based Approach to Real-Time Metro Passenger Flow anomaly detection 37

Graph Based Approach to Real-Time Metro Passenger Flow Anoma...

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37th IEEE International Conference on Data Engineering (IEEE ICDE)

作者： Zhang, Weiqi Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781728191843

Real-time anomaly detection of passenger flows in the metro system is very important to maintain the URT system's normal operation and ensure passengers' safety. This paper proposes a novel abnormal passenger flow detection method based on smart card data. The method constructs a graphic model whose topological structure can capture the spatial distribution of anomalous passenger flow. It further incorporates external information (e.g. geographical information) to depict the latent passenger flow's spatial dependence embedded in URT system. Considering abnormal flows may only exist in local regions of the metro system, a detection statistic is constructed by using graph community detection. The statistic also incorporates an adaptive sampling strategy for further signal selection and noise filter. It can be efficiently solved via a Min-Cut-based algorithm and can provide real-time solutions to anomaly detection and diagnosis. Preliminary experimental results demonstrate the efficiency of our method.

关键词： graph anomaly detection passenger flow monitoring smart card data urban rail transit system

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Detecting malicious IoT network communication through graph Neural Networks in real-world conditions

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PATTERN RECOGNITION LETTERS 2025年 189卷 92-98页

作者： Carletti, Vincenzo Foggia, Pasquale Rosa, Francesco Vento, Mario Univ Salerno Dept Informat Engn Elect Engn & Appl Math DIEM Salerno Italy Natl Res Council CNR Rome Italy

Internet of Things (IoT) devices are increasingly permeating homes, industries, and many other environments. The need for robust security measures in IoT networks has never been more critical, since they are becoming the preferred target for cyberattacks. In this paper, we address the challenge of detecting abnormal communication patterns in IoT networks using graph Neural Networks (GNNs). To this end, we have conducted a comprehensive and fair comparison of machine learning approaches and GNNs, for both static and dynamic graphs, across three recent datasets, IoT23, IoTID20, IoT Traces, that contain recordings of network communications among IoT devices in real environments. Differently from the state-of-the-art, we face the problem as anode anomaly detection task under the realistic assumption of only having normal traffic samples for training the GNNs. Furthermore, we have also restricted the false positive rate below 1% to make the system practical for human operators willing to use it as an anomaly-based IDS (A-IDS). Finally, the experimental results highlight the relevance of structural information to effectively address the task in real-world conditions.

关键词： graph anomaly detection graph Neural Networks graph-based representations IoT network security Cybersecurity

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Multiknowledge and LLM-Inspired Heterogeneous graph Neural Network for Fake News detection

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第2期12卷 682-694页

作者： Xie, Bingbing Ma, Xiaoxiao Shan, Xue Beheshti, Amin Yang, Jian Fan, Hao Wu, Jia Wuhan Univ Sch Informat Management Wuhan Peoples R China Macquarie Univ Sch Comp Sydney NSW 2019 Australia Macquarie Univ Sch Comp Sydney NSW 2019 Australia

The widespread diffusion of fake news has become a critical problem on dynamic social media worldwide, which requires effective strategies for fake news detection to alleviate its hazardous consequences for society. However, most recent efforts only focus on the features of news content and social context without realizing the benefits of large language models (LLMs) and multiple knowledge graphs (KGs), thus failing to improve detection capabilities further. To tackle this issue, we present a multiknowledge and LLM-inspired heterogeneous graph neural network for fake news detection (MiLk-FD), by combining KGs, LLMs, and graph neural networks (GNNs). Specifically, we first model news content as a heterogeneous graph (HG) containing news, entity, and topic nodes and then fuse the knowledge from three KGs to augment the factual basis of news articles. Meanwhile, we leverage TransE to initialize the knowledge features and employ LLaMa2-7B to obtain the initial feature vectors of news articles. After that, we utilize the devised HG transformer to learn news embeddings with specific feature distribution in high-dimensional spaces by aggregating neighborhood information according to metapaths. Finally, a classifier based on multilayer perceptron (MLP) is trained to predict each news article as fake or true. Through experiments, we demonstrate that our proposed framework surpasses ten baselines according to accuracy, precision, F1-score, recall, and ROC in four public real-world benchmarks (i.e., COVID-19, FakeNewsNet, PAN2020, Liar).

关键词： Deep learning fake news detection graph anomaly detection knowledge graph (KG) large language model (LLM)

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An Improved Reconstruction-Based Multiattribute Contrastive Learning for Digital-Twin-Enabled Industrial System

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3670-3679页

作者： Yang, Banglie Zhu, Linyu Dai, Cheng Garg, Sahil Kaddoum, Georges Sichuan Univ Coll Comp Chengdu 610017 Peoples R China Ecole Technol Super Elect Engn Dept Montreal PQ H3C 1K3 Canada Chitkara Univ Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 India Lebanese Amer Univ Artificial Intelligence & Cyber Syst Res Ctr Beirut 03797 Lebanon

Digital twin (DT) is a promising technology for responding to Industry 4.0 and realizing comprehensive automation and virtualization. In the Web3.0-powered 5G/6G era, the expansion of the industrial data and closer interaction among cross-industrial entities pose new security challenges for DT industrial systems. As a prevalent computing paradigm, graph anomaly detection provides an effective solution to ensure the security of DT industrial systems. However, the existing unsupervised graph anomaly detection methods tend to treat multiple graph attributes in isolation during the reconstruction process, resulting in insufficient semantics and suboptimal reconstruction performance. To overcome these challenges, we propose a multiattribute contrastive learning framework, which realizes graph anomaly detection by capturing both graph attribute patterns and their hidden relationships. First, we use an improved multiattribute aligned reconstruction approach to represent the anomaly information effectively. Besides that, the positive instance aggregation-based contrastive constraints are proposed, which can reduce the loss generated by mappings between different data dimension in feature representation space. Finally, to verify our proposal, extensive experiments have been conducted on five benchmark datasets, and the results show that our method obtains the state-of-the-art performance.

关键词： anomaly detection Contrastive learning Unsupervised learning Security graph neural networks Semantics Mutual information Internet of Things Digital twins Deep learning digital twin (DT) graph anomaly detection unsupervised learning

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