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Node Re-Ordering as a Means of Anomaly Detection in Time-Evolving Graphs [chapter]

Lida Rashidi, Andrey Kan, James Bailey, Jeffrey Chan, Christopher Leckie, Wei Liu, Sutharshan Rajasegarar, Kotagiri Ramamohanarao
2016 Lecture Notes in Computer Science  
In this paper, we address the problem of anomaly detection in time-evolving graphs, where graphs are a natural representation for data in many types of applications.  ...  In this paper, we focus on detecting anomalies in a sequence of graphs based on rank correlations of the reordered nodes.  ...  Anomaly detection in time-evolving graphs is the task of finding timestamps that correspond to an unusual event in a sequence of graphs [2] .  ... 
doi:10.1007/978-3-319-46227-1_11 fatcat:ip6neqrse5fedkcuo5lyayfrii

PredictDeep: Security Analytics as a Service for Anomaly Detection and Prediction

Marwa A. Elsayed, Mohammad Zulkernine
2020 IEEE Access  
We leverage graph embeddings to represent the nodes and relationships in the graph model as feature vectors to learn and predict anomalies in an inductive way utilizing recent advanced deep graph neural  ...  It represents the collected data and transforms them into a graph model. The graph model captures the analytical activities as well as their interrelation.  ...  Such a model can generalize well to the graph expansion and induce the embeddings of unseen nodes. This, in turn, alleviates the need of re-training the model when new nodes join the graph.  ... 
doi:10.1109/access.2020.2977325 fatcat:krsyelxanrbetmkilqnpv63rt4

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning [article]

Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu
2021 arXiv   pre-print
a single graph, or anomalous graphs in a database/set of graphs.  ...  In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.  ...  In their proposed model, DeepSphere, a dynamic graph is described as a collection of three-order tensors, {X k , k = 1, 2...} where each X ∈ R N ×N ×T , and the slices along the time dimension are the  ... 
arXiv:2106.07178v4 fatcat:efargsqnxndqbfqat2q5iz54u4

Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs

Emaad Manzoor, Sadegh M. Milajerdi, Leman Akoglu
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
Given a stream of heterogeneous graphs containing di↵erent types of nodes and edges, how can we spot anomalous ones in real-time while consuming bounded memory?  ...  and flagging anomalies in real time.  ...  Any conclusions expressed in this material are of the authors and do not necessarily reflect the views, expressed or implied, of the funding parties.  ... 
doi:10.1145/2939672.2939783 dblp:conf/kdd/ManzoorMA16 fatcat:673eg4mtljbc7expjrvixogbdu

Graph based anomaly detection and description: a survey

Leman Akoglu, Hanghang Tong, Danai Koutra
2014 Data mining and knowledge discovery  
As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data.  ...  This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs.  ...  Any findings and conclusions expressed in this material are those of the author(s) and do not necessarily reflect the position or the policy of the U.S.  ... 
doi:10.1007/s10618-014-0365-y fatcat:rfjn7bwdgra5faorwbdkkb45ze

Graph-based Anomaly Detection and Description: A Survey [article]

Leman Akoglu and Hanghang Tong and Danai Koutra
2014 arXiv   pre-print
As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data.  ...  This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs.  ...  Any findings and conclusions expressed in this material are those of the author(s) and do not necessarily reflect the position or the policy of the U.S.  ... 
arXiv:1404.4679v2 fatcat:y6nsswymcfc2pa7qe7zrjzc7wq

A Survey on Social Media Anomaly Detection [article]

Rose Yu, Huida Qiu, Zhen Wen, Ching-Yung Lin, Yan Liu
2016 arXiv   pre-print
While a large amount of work have been dedicated to traditional anomaly detection problems, we observe a surge of research interests in the new realm of social media anomaly detection.  ...  Social media anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination.  ...  Sanjay Chawla from University of Sydney for his encouragement and detailed advice on the formulation of the anomaly categorization, distinguish between the data error and true anomalies.  ... 
arXiv:1601.01102v2 fatcat:o6332bw3ureado4mynawpyeuqu

HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams [article]

Geon Lee, Minyoung Choe, Kijung Shin
2022 arXiv   pre-print
We propose HashNWalk, an incremental algorithm that detects anomalies in a stream of hyperedges.  ...  Despite their broad potential applications, anomaly detection in hypergraphs (i.e., sets of hyperedges) has received surprisingly little attention, compared to that in graphs.  ...  As many real-world graphs evolve over time, detecting anomalies in real-time, as they appear, is desirable [9, 18] .  ... 
arXiv:2204.13822v2 fatcat:nmangkvaofftlaxfsjpugkpa5e

GraphSIF: analyzing flow of payments in a Business-to-Business network to detect supplier impersonation

Rémi Canillas, Omar Hasan, Laurent Sarrat, Lionel Brunie
2020 Applied Network Science  
We propose to use a graph-based approach to design an Anomaly Detection System (ADS) based on a Self-Organizing Map (SOM) allowing us to label a suspicious transaction as either legitimate or fraudulent  ...  In this paper, we propose a data-driven fraud detection system whose goal is to provide an accurate estimation of financial transaction legitimacy by using the knowledge contained in the network of transactions  ...  Acknowledgements The authors would like to acknowledge the help of the development team of SiS-id : François Agier & Kévin Lainte.  ... 
doi:10.1007/s41109-020-00283-1 fatcat:7esnki2tbnd6lpebrdtrjy72o4

Time Series Network Data Enabling Distributed Intelligence. A Holistic IoT Security Platform Solution

Aikaterini Protogerou, Evangelos V. Kopsacheilis, Asterios Mpatziakas, Kostas Papachristou, Traianos Ioannis Theodorou, Stavros Papadopoulos, Anastasios Drosou, Dimitrios Tzovaras
2022 Electronics  
nodes are processed by a graph neural network algorithm.  ...  Network traffic data are fed to the AI agents, which process consecutive traffic samples from the network in a time series analysis manner, where consecutive time windows framing the traffic of the surrounding  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/electronics11040529 fatcat:m4tgpyvzhrft3i5loro3hememm

Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection [article]

Yuhang Wu, Mengting Gu, Lan Wang, Yusan Lin, Fei Wang, Hao Yang
2021 arXiv   pre-print
More concretely, we model time series as one type of nodes, and the time series segments (regarded as event) as another type of nodes, where the edge between two types of nodes describe a temporal pattern  ...  Based on this design, relations between time series can be explicitly modelled via dynamic connections to event nodes, and the multivariate time-series anomaly detection problem can be formulated as a  ...  INTRODUCTION Detecting anomalies in time-series data has been an important problem in the research community of data mining as well as the finance industry.  ... 
arXiv:2108.06783v1 fatcat:ygwbiwt6bjct7cui7wjhtvunay

Design of an Anomaly-based Threat Detection & Explication System

Robert Luh, Sebastian Schrittwieser, Stefan Marschalek, Helge Janicke
2017 Proceedings of the 3rd International Conference on Information Systems Security and Privacy  
In this paper, we propose a system able to explain anomalous behavior within a user session by considering anomalies identified through their deviation from a set of baseline process graphs.  ...  We prototypically implement smart anomaly explication through a number of competency questions derived and evaluated using the decision tree algorithm.  ...  ACKNOWLEDGMENTS The financial support by the Austrian Federal Ministry of Science, Research and Economy and the National Foundation for Research, Technology and Development is gratefully acknowledged.  ... 
doi:10.5220/0006205203970402 dblp:conf/icissp/LuhSMJ17 fatcat:spw5btpfnfcrnetgwsenkzllu4

Visual analysis of large-scale network anomalies

Q. Liao, L. Shi, C. Wang
2013 IBM Journal of Research and Development  
In this paper, we provide a brief overview of several useful visualization techniques for the analysis of spatiotemporal anomalies in large-scale networks.  ...  CG and HDPG are used to examine the complex relationship of data dimensions among graph nodes through transformation in a high-dimensional space.  ...  Aaron Striegel for his support at the University of Notre Dame, as well as colleagues at IBM Research -China.  ... 
doi:10.1147/jrd.2013.2249356 fatcat:foajq5zrmzhkfl4rnjh7hvdzei

Design and implementation of small multiples matrix-based visualisation to monitor and compare email socio-organisational relationships

Mithileysh Sathiyanarayanan, Cagatay Turkay, Odunayo Fadahunsi
2018 2018 10th International Conference on Communication Systems & Networks (COMSNETS)  
We would also like to thank people from the giCentre, City, University of London, UK for their timely guidance.  ...  ACKNOWLEDGMENT We would like to thank Rahul Powar and Randal Pinto of Red Sift Research for their insightful comments and enlightening us with their corporate knowledge and supporting us by funding.  ...  This is a complex dataset, but the presented approach breaks down the data in a year over year format to make the data extremely manageable in order to study for insights. 2) Anomaly detection in social  ... 
doi:10.1109/comsnets.2018.8328288 dblp:conf/comsnets/Sathiyanarayanan18 fatcat:sptzmvfq4fgsbed3x2t3una3ke

Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

Aqeel Feroze, Ali Daud, Tehmina Amjad, Malik Khizar Hayat
2021 SN Computer Science  
Anomaly detection has evolved as a successful research subject in the areas such as bibliometrics, informatics and computer networks including security-based and social networks.  ...  In this research, we bifurcated group anomaly detection techniques into activity-based and graph-based methods.  ...  Sequence/Window-Based Techniques It is used to detect anomalous patterns that are related to a time-evolving window.  ... 
doi:10.1007/s42979-021-00603-x fatcat:oyjzthza7vbhnakpm3t2ko6ctq
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