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AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks [article]

Haoyi Fan, Fengbin Zhang, Zuoyong Li
2020 arXiv   pre-print
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion  ...  In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure  ...  structure and node attribute on attributed network for anomaly detection.  ... 
arXiv:2002.03665v2 fatcat:oqaukl3dz5dd7i3s6jna254ahq

AnomMAN: Detect Anomaly on Multi-view Attributed Networks [article]

Ling-Hao Chen, He Li, Wenhao Yang
2022 arXiv   pre-print
However, most work tries to detect anomalies on attributed networks only considering a single interaction action, which cannot consider rich kinds of interaction actions in multi-view attributed networks  ...  Anomaly detection on attributed networks is widely used in web shopping, financial transactions, communication networks, and so on.  ...  Anomaly Detection on Attributed Network As attributed network is introduced to model complex systems, many studies are concentrated on anomaly detection on attributed networks.  ... 
arXiv:2201.02822v1 fatcat:34e7qd4hebezdh4tqe3z5yzap4

DeepAD: A Joint Embedding Approach for Anomaly Detection on Attributed Networks [chapter]

Dali Zhu, Yuchen Ma, Yinlong Liu
2020 Lecture Notes in Computer Science  
Extensive experiments on real-world attributed networks demonstrate the effectiveness of our proposed anomaly detection approach.  ...  Detecting anomalies in the attributed network is a vital task that is widely used, ranging from social media, finance to cybersecurity.  ...  Recently, there is emerging research of anomaly detection focusing on attributed networks due to the potential rich information contained in the attributed network.  ... 
doi:10.1007/978-3-030-50417-5_22 fatcat:bafduynoqbbd5m5bps4jshcgke

Temporal Patterns Discovery of Evolving Graphs for Graph Neural Network (GNN)-based Anomaly Detection in Heterogeneous Networks

Jongmo Kim, Kunyoung Kim, Gi-Yoon Jeon, Mye M. Sohn
2022 Journal of Internet Services and Information Security  
This paper proposes a new method named evolving-graph generation framework to simultaneously solve the complexity and dynamic nature of the attribute networks that can occur in graph-based anomaly detection  ...  To show the superiority of the proposed framework, we conduct experiments and evaluations on 8 real-world datasets with anomaly labels with comparative state-of-the-art models of graph-based anomaly detection  ...  Acknowledgements Temporal patterns discovery of evolving graphs for GNN-based anomaly detection J. Kim, K. Kim, G. Jeon, and M.  ... 
doi:10.22667/jisis.2022.02.28.072 dblp:journals/jisis/KimKJS22 fatcat:uncpemjenbgavni65vrnhzszzy

Interactive Visualization of Network Anomalous Events [chapter]

Yang Cai, Rafael de M. Franco
2009 Lecture Notes in Computer Science  
This technology is expected to have an impact on visual real-time data mining for network security, sensor networks and many other multivariable real-time monitoring systems.  ...  We present an interactive visualization and clustering algorithm that reveals real-time network anomalous events.  ...  This provides a visual way to interpret the network flow and gives a human expert the possibility to make the final decision on the detection of an anomaly.  ... 
doi:10.1007/978-3-642-01970-8_44 fatcat:o2prseaggjdcrmknsqkvmeckqu

Radar: Residual Analysis for Anomaly Detection in Attributed Networks

Jundong Li, Harsh Dani, Xia Hu, Huan Liu
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process.  ...  In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection  ...  Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2017/299 dblp:conf/ijcai/LiDHL17 fatcat:7etefb25zrhxvma4mpymesk7uy

Bridging the Gap of Network Management and Anomaly Detection through Interactive Visualization

Tao Zhang, Qi Liao, Lei Shi
2014 2014 IEEE Pacific Visualization Symposium  
Figure 1 : The overview of web-based visualization tool for analyzing the network and system anomalies in standard log files.  ...  While each individual view is well understood, the effects of such views in analyzing network anomalies are not well studied.  ...  events and network anomaly detection.  ... 
doi:10.1109/pacificvis.2014.22 dblp:conf/apvis/ZhangLS14 fatcat:4sazpedaubektc6w7pwszwmbpe


Pallavi Raj, Rakhi Garg
2020 Indian Journal of Computer Science and Engineering  
This paper mainly focuses on the graph mining techniques used for anomaly detection in social networks.  ...  Using anomaly detection techniques, we can identify the unusual behavior of such users. In social networks, anomalies can be detected by exploring the pattern hidden in the network.  ...  Types of graph used Based on the information available in a graph, the graph is divided into labeled/attributed graph and unlabeled/unattributed graph.In labeled/attributed grapd, anomalies are detected  ... 
doi:10.21817/indjcse/2020/v11i1/201101005 fatcat:ynz45figozbu7fuznqo2u3j55y

GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks [article]

Venus Haghighi, Behnaz Soltani, Adnan Mahmood, Quan Z. Sheng, Jian Yang
2022 arXiv   pre-print
Traditional approaches cannot be adopted on attributed networks' settings to solve the problem of anomaly detection.  ...  Anomaly detection in attributed networks has received a considerable attention in recent years due to its applications in a wide range of domains such as finance, network security, and medicine.  ...  OddBall [5] detects anomalies based on the egonet patterns in plain networks.  ... 
arXiv:2207.03688v1 fatcat:ry2irkx2drdbdoalcy3zkltfqe

Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks [article]

Fengbin Zhang, Haoyi Fan, Ruidong Wang, Zuoyong Li, Tiancai Liang
2021 arXiv   pre-print
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  ...  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  ...  | Anomaly Detection on Attributed Networks Recently, anomaly detection on attributed networks has attracted lots of research interests [18] , whose goal is to detect the anomalies by obtaining information  ... 
arXiv:2109.00138v1 fatcat:2hwfpi2dkvc27g35y7moiseule

ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks [article]

Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy
2020 arXiv   pre-print
Extensive experiments on several real-world attributed networks demonstrate the effectiveness of ResGCN in detecting anomalies.  ...  Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection.  ...  ., 2018) , we formulate the task of anomaly detection on attributed networks: Problem 1 Anomaly Detection on Attributed Networks.  ... 
arXiv:2009.14738v1 fatcat:v57wi734sjfz7jl7m4fid6x7yi

Anomaly detection in online social networks [article]

David Savage, Xiuzhen Zhang, Xinghuo Yu, Pauline Chou, Qingmai Wang
2016 arXiv   pre-print
In this paper we survey existing computational techniques for detecting anomalies in online social networks.  ...  We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this  ...  A workshop on the detection of network based anomalies was also held at ACM 2013 .  ... 
arXiv:1608.00301v1 fatcat:fwdnk2bdzvhyrnnntbb65ao6d4

Anomaly Analysis Technology Based on Deterministic Characteristics of Intranet

Zhiwen Chen, Guihua Wang, Weiyan Zhang, Dali Zhou, Yansong Wang
2018 MATEC Web of Conferences  
Based on the network state and behavior collection and analysis network dynamic characteristics, combined with the deterministic feature priori knowledge of the network, an anomaly analysis model which  ...  Based on the model design, a traffic-based anomaly analysis system is implemented. The system can effectively find a variety of high-risk anomalies in the intranet.  ...  Through the simulation of the actual environment, the traffic based anomaly analysis method based on the network deterministic feature can effectively detect a variety of anomalies, especially forging  ... 
doi:10.1051/matecconf/201823201030 fatcat:po7undxeubbkdolq7hgjcutxt4

SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks [article]

Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, Na Zou
2019 arXiv   pre-print
Anomaly detection in attributed networks has various applications such as monitoring suspicious accounts in social media and financial fraud in transaction networks.  ...  ., in many practical scenarios, links describing instance-to-instance dependencies and interactions are available. Such systems are called attributed networks.  ...  Anomaly Detection in Attributed Networks We propose a Spectral autoencoder based anomaly detection framework -SpecAE, for attributed networks. The pipeline of SpecAE is illustrated in Fig.1 .  ... 
arXiv:1908.03849v3 fatcat:qdhuhlfyffdnjiaxlrx2izpocy

Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks [article]

Jiaqiang Zhang, Senzhang Wang, Songcan Chen
2022 arXiv   pre-print
on attributed networks.  ...  Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security.  ...  ., 2019] conducts the investigation on the problem of anomaly detection on attributed networks.  ... 
arXiv:2205.04816v1 fatcat:r6v76enibnaqzne3hrgbv6i3oi
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