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.

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Title An embedding scheme for detecting anomalous block structured graphs
Author(s) Rashidi, Lida
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736
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
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