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

Wang, Xuhong; Jin, Baihong; Du, Ying; Cui, Ping; Yang, Tan; Yang, Yupu

doi:10.1007/s00521-021-05924-9

Cited by 71 publications

(52 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Public datasets/software GNN-based solutions Point anomaly Secure water treatment (SWaT), water distribution system (WADI), and critical infrastructure security showdown (CISS) datasets [123], [124], [125] Contextual anomaly SWaT, WADI and BATADAL datasets, as well as the Xcos software and epanetCPA toolbox [124], [125], [126], [127], [128] Collective anomaly LITNET-2020, M2M Using OPC UA, WUSTL-IIoT-2018 and KDD 1999 datasets, as well as the Xcos software and the epanetCPA tool [129], [130], [131] fault flaw overheat defect Fig. 6.…”

Section: Type Of Anomaliesmentioning

confidence: 99%

“…Due to the popularity of one class support vector machine in detecting outliers, Wang et al [123] generalized it to graph data and proposed one class graph neural network (OCGNN) that is a one class classification framework for detecting anomalies in graph data. OCGNN can achieve the well-known one class objective using the powerful representation ability of GNN.…”

Section: A Pointmentioning

confidence: 99%

See 1 more Smart Citation

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

View full text Add to dashboard Cite

The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

show abstract

Section: Type Of Anomaliesmentioning

confidence: 99%

Section: A Pointmentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

View full text Add to dashboard Cite

show abstract

“…Although those methods achieve successful applications in the traditional anomaly detection domain, they cannot generalize well to the graph data, where the structure patterns among data points are important. 21,38 Therefore, the problem of anomaly detection on graph data still be an open problem.…”

Section: Traditional Anomaly Detectionmentioning

confidence: 99%

“…To alleviate above-mentioned problems, inspired by the successful application of hypersphere learning in anomaly detection, [19][20][21] in this paper, we propose an end-to-end one-class classification-based anomaly detection framework for attributed networks, named Dual Support Vector Data description based AutoEncoder (Dual-SVDAE), which aims at learning the compact hypersphere boundary of normal nodes' latent space from both the structure and attribute perspectives. Specifically, Dual-SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space, respectively.…”

Section: Introductionmentioning

confidence: 99%

Deep Dual Support Vector Data description for anomaly detection on attributed networks

Zhang

Fan

Wang

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Networks are ubiquitous in the real world such as social networks and communication networks, and anomaly detection on networks aims at finding nodes whose structural or attributed patterns deviate significantly from the majority of reference nodes. However, most of the traditional anomaly detection methods neglect the relation structure information among data points and therefore cannot effectively generalize to the graph structure data. In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks. Specifically, Dual-SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space, respectively.Then, a dual-hypersphere learning mechanism is imposed on them to learn two hyperspheres of normal nodes from the structure and attribute perspectives, respectively. Moreover, to achieve joint learning between the structure and attribute of the network, we fuse the structure embedding and attribute embedding as the final input of the feature decoder to generate the node attribute. Finally, abnormal nodes can be

show abstract

“…This is used for vertex classification, link prediction, graph classification, etc. [197,210]. Wang et al [197] propose a one class classification framework for graph anomaly detection using Graph Neural Networks.…”

Section: Cc(u) =mentioning

confidence: 99%

A Framework for Anomaly Detection under Dynamic and Distributed Scenarios

Odiathevar¹

View full text Add to dashboard Cite

Anomaly Detection is an important aspect of many application domains. It refers to the problem of finding patterns in data that do not conform to expected behaviour. Hence, understanding of expected behaviour well is fundamental to performing effective anomaly detection. However, data profiles constantly evolve in certain domains such as computer networks. In other domains such as traffic monitoring and healthcare, data are distributed and are either too large or there are privacy concerns in transmitting them to a central location. These situations pose a challenge to obtain an accurate understanding of non-anomalous profiles. Changing profiles undermine existing anomaly detection models and make them less effective. Training a robust model with data from multiple sources is also challenging. Moreover, in real world scenarios, it is not apparent how an anomaly detection model can be built to address the problem. This thesis focuses on the building of a robust anomaly detection system where data profiles evolve and/or are distributed. It proposes a novel Online Offline Framework to separate existing expected behaviour, new possible expected behaviour and anomalies in streaming data. It also addresses the distributed scenario using a theoretically sound fully Bayesian approach. These methods improve performances of anomaly detection systems and work well with biased and uneven data partitions. The proposed methods are validated using real world data in three different domains. This thesis identifies the implementation difficulties in these domains and produces three novel methodologies to address each of the core anomaly detection problems.

show abstract

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

Cited by 71 publications

References 27 publications

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Deep Dual Support Vector Data description for anomaly detection on attributed networks

A Framework for Anomaly Detection under Dynamic and Distributed Scenarios

Contact Info

Product

Resources

About