An embedding scheme for detecting anomalous block structured graphs Rashidi, Lida, Rajasegarar, Sutharshan and Leckie, Christopher 2015, An embedding scheme for detecting anomalous block structured graphs, in Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015. Proceedings, Part II, Springer, Cham, Switzerland, pp. 215-227, doi: 10.1007/978-3-319-18032-8_17. Attached Files Name Description MIMEType Size Downloads Title An embedding scheme for detecting anomalous block structured graphs Author(s) Rashidi, Lida Rajasegarar, Sutharshan Leckie, Christopher Conference name Knowledge Discovery and Data Mining. Pacific-Asia Conference (19th : 2015 : Ho Chi Minh City, Vietnam) Conference location Ho Chi Minh City, Vietnam Conference dates 19-22 May 2015 Title of proceedings Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015. Proceedings, Part II Publication date 2015 Series Lecture Notes in Computer Science Start page 215 End page 227 Total pages 13 Publisher Springer Place of publication Cham, Switzerland Keyword(s) anomaly detection block structured graph one-class SVM random projection embedding Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science, Theory & Methods Computer Science NETWORKS Summary Graph-based anomaly detection plays a vital role in various application domains such as network intrusion detection, social network analysis and road traffic monitoring. Although these evolving networks impose a curse of dimensionality on the learning models, they usually contain structural properties that anomaly detection schemes can exploit. The major challenge is finding a feature extraction technique that preserves graph structure while balancing the accuracy of the model against its scalability. We propose the use of a scalable technique known as random projection as a method for structure aware embedding, which extracts relational properties of the network, and present an analytical proof of this claim. We also analyze the effect of embedding on the accuracy of one-class support vector machines for anomaly detection on real and synthetic datasets. We demonstrate that the embedding can be effective in terms of scalability without detrimental influence on the accuracy of the learned model. ISBN 9783319180311 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-18032-8_17 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2015, Springer International Publishing Persistent URL http://hdl.handle.net/10536/DRO/DU:30084914 Document type: Conference Paper Collection: School of Information Technology Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 14 Jul 2016, 17:09:17 EST Tue, 29 Nov 2016, 13:13:54 EST Wed, 30 Nov 2016, 04:00:40 EST Wed, 30 Nov 2016, 05:05:25 EST Fri, 02 Dec 2016, 09:50:32 EST Fri, 02 Dec 2016, 09:53:06 EST Thu, 22 Feb 2018, 14:14:00 EST Thu, 22 Feb 2018, 14:14:54 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 93 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Tue, 29 Nov 2016, 13:13:54 EST

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