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Anomaly detection in data represented as graphs

William Eberle, Lawrence Holder
<span title="2007-11-09">2007</span> <i title="IOS Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lm7wouyrqzbgvenkjxkrtwootu" style="color: black;">Intelligent Data Analysis</a> </i> &nbsp;
An important area of data mining is anomaly detection, particularly for fraud. However, little work has been done in terms of detecting anomalies in data that is represented as a graph.  ...  In this paper, we validate all three approaches using synthetic data, verifying that each of the algorithms on graphs and anomalies of varying sizes, are able to detect the anomalies with very high detection  ...  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/ida-2007-11606">doi:10.3233/ida-2007-11606</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jkglslwoobhjvowebm2m4lcxma">fatcat:jkglslwoobhjvowebm2m4lcxma</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808131014/http://www.ailab.wsu.edu/subdue/papers/EberleIDA07.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/31/21/312138de46a57337a1baab9912657d5355629f4f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/ida-2007-11606"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

ANOMALY DETECTION IN TEXT DATA THAT REPRESENTED AS A GRAPH USING DBSCAN ALGORITHM

Khazaal Asma, Abdulsahib
<span title="">2017</span> <span class="release-stage">unpublished</span>
As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies.  ...  Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG).  ...  For comprehension the procedure of DBSCAN, must be presented the how this algorithm works in data represented as a graph.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lnmszvzoonhq3pv3zmqbgg4n24">fatcat:lnmszvzoonhq3pv3zmqbgg4n24</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180410232806/http://www.jatit.org/volumes/Vol95No9/22Vol95No9.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3a/db/3adb2f286243398d859de846d280268bd58d4bd9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a>

A partitioning approach to scaling anomaly detection in graph streams

William Eberle, Lawrence Holder
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/faqqmambavbalpofpx3p6nntua" style="color: black;">2014 IEEE International Conference on Big Data (Big Data)</a> </i> &nbsp;
reasonable levels of effectiveness in detecting anomalies.  ...  In this paper we present a partitioning and windowing approach that partitions the graph as it streams in over time and maintains a set of normative patterns and anomalies.  ...  In addition, they have not dealt with the scalability issues associated with "big data" when attempting to learn patterns and anomalies in data represented as a graph.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bigdata.2014.7004367">doi:10.1109/bigdata.2014.7004367</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bigdataconf/EberleH14.html">dblp:conf/bigdataconf/EberleH14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uuozvojevngillq4v7nc3yfpwm">fatcat:uuozvojevngillq4v7nc3yfpwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808055126/http://ailab.wsu.edu/adgs/pdfs/EberleBigData14.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d5/56/d5564988fcaf6c608b76d48df93c753fbe97bf4e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bigdata.2014.7004367"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Anomaly, event, and fraud detection in large network datasets

Leman Akoglu, Christos Faloutsos
<span title="">2013</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/puezkhxc3rggrgb456avsvxi34" style="color: black;">Proceedings of the sixth ACM international conference on Web search and data mining - WSDM &#39;13</a> </i> &nbsp;
The goal of this tutorial is to provide a general, comprehensive overview of the state-of-the-art methods for anomaly, event, and fraud detection in data represented as graphs.  ...  As objects in graphs have long-range correlations, novel technology has been developed for abnormality detection in graph data.  ...  Through this tutorial, the participants will learn important techniques to attack the anomaly detection problem in data represented as graphs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2433396.2433496">doi:10.1145/2433396.2433496</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wsdm/AkogluF13.html">dblp:conf/wsdm/AkogluF13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i7m6c3g7j5agvnova3javx3aly">fatcat:i7m6c3g7j5agvnova3javx3aly</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809000442/http://pub.cs.sunysb.edu/~leman/wsdm13/13-wsdm-tutorial.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a5/20/a520e1ec9cea03e125af46c53f9aea2df9848979.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2433396.2433496"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

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
<span title="2021-10-11">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Graphs have been prevalently used to represent the structural information, which raises the graph anomaly detection problem - identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in  ...  Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others.  ...  are represented as color histograms [21] ), and then detect outlying data points in the vector space [22] - [24] , as shown in Fig. 1(a) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.07178v4">arXiv:2106.07178v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/efargsqnxndqbfqat2q5iz54u4">fatcat:efargsqnxndqbfqat2q5iz54u4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211014153321/https://arxiv.org/pdf/2106.07178v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5e/4c/5e4c5f7506a1bb8aab0a9d5168674766eb54a91f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.07178v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Streaming data analytics for anomalies in graphs

William Eberle, Lawrence Holder
<span title="">2015</span> <i title="IEEE"> 2015 IEEE International Symposium on Technologies for Homeland Security (HST) </i> &nbsp;
We evaluate our approach on a dataset that represents people movements and actions, as well as a scalable, streaming data generator that represents social network behaviors, in order to assess the ability  ...  One potential solution to the issue of handling very large graphs is to handle data as a "stream".  ...  The GBAD approach is based on the exploitation of structure in data represented as a graph.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ths.2015.7225259">doi:10.1109/ths.2015.7225259</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b4nwdmjm6renle7iyre26czkse">fatcat:b4nwdmjm6renle7iyre26czkse</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808032424/http://www.ailab.wsu.edu/adgs/pdfs/EberleHST2015.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0a/82/0a8214783a262dced7e6c21bc6b02486d86d9250.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ths.2015.7225259"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Visualization of Anomalies using Graph-Based Anomaly Detection

Ramesh Paudel, Lauren Tharp, Dulce Kaiser, William Eberle, Gerald Gannod
<span title="2021-04-18">2021</span> <i title="University of Florida George A Smathers Libraries"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qsmy2pq4ofbv7pwhg3dhn3kmmy" style="color: black;">Proceedings of the ... International Florida Artificial Intelligence Research Society Conference</a> </i> &nbsp;
We present an approach for visualizing anomalies using a graph-based anomaly detection methodology that aims to provide visual context to network traffic.  ...  of data that must be analyzed.  ...  Anomaly Detection Approach The idea behind the graph-based anomaly detection (GBAD) approach used in this work is to discover anomalies in graph-based data where the anomalous substructure in a graph is  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32473/flairs.v34i1.128554">doi:10.32473/flairs.v34i1.128554</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tuxdozqz4za6haydxfovdlpsmm">fatcat:tuxdozqz4za6haydxfovdlpsmm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210514132105/https://journals.flvc.org/FLAIRS/article/download/128554/130030" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/de/fd/defdb365104dedb9876e46ee068f37ab3bbeb0e4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32473/flairs.v34i1.128554"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Incremental Anomaly Detection in Graphs

William Eberle, Lawrence Holder
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gckg3mzs4fhxhbrvmbsa54bccm" style="color: black;">2013 IEEE 13th International Conference on Data Mining Workshops</a> </i> &nbsp;
In this work, we describe methods for graph-based anomaly detection via graph partitioning and windowing, and demonstrate their ability to efficiently detect anomalies in data represented as a graph.  ...  However, current approaches to detecting anomalies in graphs are computationally expensive and do not scale to large graphs.  ...  The GBAD approach is based on the exploitation of structure in data represented as a graph.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdmw.2013.93">doi:10.1109/icdmw.2013.93</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icdm/EberleH13.html">dblp:conf/icdm/EberleH13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yoo5l5bufrgolfvgzozcrb5up4">fatcat:yoo5l5bufrgolfvgzozcrb5up4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808055153/http://ailab.wsu.edu/adgs/pdfs/EberleICDM2013.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2f/76/2f766ff3a4bc6190565b95c912656ee8ef94ba80.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdmw.2013.93"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Spectral anomaly detection using graph-based filtering for wireless sensor networks

Hilmi E. Egilmez, Antonio Ortega
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</a> </i> &nbsp;
In particular, we consider the problem of unsupervised data anomaly detection over wireless sensor networks (WSNs) where sensor measurements are represented as signals on a graph.  ...  The associated graph-based filters are then employed to project the graph signals on normal and anomaly subspaces, and resulting projections are used in detection of data anomalies.  ...  As discussed in Section 1, the spectral anomaly detection problem boils down to defining subspaces that represent the normal and anomalous regions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2014.6853764">doi:10.1109/icassp.2014.6853764</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/EgilmezO14.html">dblp:conf/icassp/EgilmezO14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fk5tuebl7faopefff67zxiquei">fatcat:fk5tuebl7faopefff67zxiquei</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20151009051612/http://www-scf.usc.edu/~hegilmez/Papers/icassp2014.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b6/bb/b6bb78aab0b2ba41db56d8c196c49a43462eca76.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2014.6853764"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Insider Threat Detection Using Graph-Based Approaches

William Eberle, Lawrence Holder
<span title="">2009</span> <i title="IEEE"> 2009 Cybersecurity Applications &amp; Technology Conference for Homeland Security </i> &nbsp;
This definition of an anomaly is unique in the arena of graph-based anomaly detection, as well as non-graph-based anomaly detection.  ...  Graph-based data mining approaches analyze data that can be represented as a graph (i.e., vertices and edges).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/catch.2009.7">doi:10.1109/catch.2009.7</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6uyhmua77rdgnhfvudnzka63z4">fatcat:6uyhmua77rdgnhfvudnzka63z4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20100606013720/http://www.eecs.wsu.edu/~holder/pubs/EberleCATCH09.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b7/8c/b78c521a3dd1565738c641696303cb8d4ccb81ea.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/catch.2009.7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

SOME OBSERVATION OF ALGORITHMS DEVELOPED FOR ANOMALY DETECTION

Pallavi Raj, Rakhi Garg
<span title="2020-02-29">2020</span> <i title="ENGG Journals Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zjfzi457cfayfoyo7qazzibsha" style="color: black;">Indian Journal of Computer Science and Engineering</a> </i> &nbsp;
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.  ...  Graph based anomaly detection is the process of detecting anomalies from the data that are represented as a graph [Eberle and Holder, (2007) ].  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21817/indjcse/2020/v11i1/201101005">doi:10.21817/indjcse/2020/v11i1/201101005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ynz45figozbu7fuznqo2u3j55y">fatcat:ynz45figozbu7fuznqo2u3j55y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200507152348/http://www.ijcse.com/docs/INDJCSE20-11-01-005.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bc/d0/bcd0db3f6128df1d1c18758a2f323774fc64cf24.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21817/indjcse/2020/v11i1/201101005"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks [article]

Timothy LaRock, Vahan Nanumyan, Ingo Scholtes, Giona Casiraghi, Tina Eliassi-Rad, Frank Schweitzer
<span title="2020-01-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Anomaly detection has, in fact, been extensively studied in categorical sequences. However, we often have access to time series data that represent paths through networks.  ...  The unsupervised detection of anomalies in time series data has important applications in user behavioral modeling, fraud detection, and cybersecurity.  ...  Temporal Anomaly Detection in Graphs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.10580v2">arXiv:1905.10580v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kcdt36lupncohi623ncku5u2tm">fatcat:kcdt36lupncohi623ncku5u2tm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321021334/https://arxiv.org/pdf/1905.10580v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.10580v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey on Different Graph Based Anomaly Detection Techniques

Debajit Sensarma, Samar Sen Sarma
<span title="2015-11-11">2015</span> <i title="Indian Society for Education and Environment"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wffwpj3q45g5zfjzfeyagk5uea" style="color: black;">Indian Journal of Science and Technology</a> </i> &nbsp;
to improve the technique of detecting anomalies in data has been given.  ...  This survey paper cites some methods of graph based anomaly detection in the field of information security, finance, cybersecurity, online social networks, health care, law enforcement etc. and their classification  ...  In this work, anomaly detection methods for detecting anomalous data where data represented as graphs are depicted in a nutshell and it contains a short review of recent existing graph based anomaly detection  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17485/ijst/2015/v8i1/75197">doi:10.17485/ijst/2015/v8i1/75197</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2eckrpzh6va7dmixqooukngtly">fatcat:2eckrpzh6va7dmixqooukngtly</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720183312/http://www.indjst.org/index.php/indjst/article/download/75197/65345" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17485/ijst/2015/v8i1/75197"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Self-Supervised and Interpretable Anomaly Detection using Network Transformers [article]

Daniel L. Marino, Chathurika S. Wickramasinghe, Craig Rieger, Milos Manic
<span title="2022-02-25">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Machine Learning (ML) and Deep Neural Networks (DNNs) have been proposed in the past as a tool to identify anomalies in computer networks.  ...  In this paper, we introduce the Network Transformer (NeT), a DNN model for anomaly detection that incorporates the graph structure of the communication network in order to improve interpretability.  ...  Hierarchical Graph Features Global features: -Represent the entire graph. -Used as first line of indication for anomaly detection. Node features: -Represent individual devices.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.12997v1">arXiv:2202.12997v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sfc2fril45hv5lemcnoyltc7ly">fatcat:sfc2fril45hv5lemcnoyltc7ly</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220521132123/https://arxiv.org/pdf/2202.12997v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/22/a6/22a6df6d8d16f7c8a976e1da2e811f974934ca3e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.12997v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Detecting Anomalies in Cargo Using Graph Properties [chapter]

William Eberle, Lawrence Holder
<span title="">2006</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Here, we analyze the use of graph properties as a method for uncovering anomalies in data represented as a graph.  ...  The ability to mine relational data has become important in several domains (e.g., counter-terrorism), and a graph-based representation of this data has proven useful in detecting various relational, structural  ...  We show that differences in graph properties between normal graphs and those intentionally altered can detect anomalies.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11760146_108">doi:10.1007/11760146_108</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z2r3o64zu5avvgh4aqys5bmdbq">fatcat:z2r3o64zu5avvgh4aqys5bmdbq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20100605234912/http://www.eecs.wsu.edu/~holder/pubs/EberleISI06.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fd/69/fd69c058e0b070edcb5624a80fbc77b1fb264d1c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11760146_108"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>
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