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An embedding scheme for detecting anomalous block structured graphs
conference contribution
posted on 2015-05-09, 00:00 authored by L Rashidi, Sutharshan RajasegararSutharshan Rajasegarar, C LeckieGraph-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.
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Event
Knowledge Discovery and Data Mining. Pacific-Asia Conference (19th : 2015 : Ho Chi Minh City, Vietnam)Volume
9078Series
Lecture Notes in Computer SciencePagination
215 - 227Publisher
SpringerLocation
Ho Chi Minh City, VietnamPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2015-05-19End date
2015-05-22ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319180311Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2015, Springer International PublishingTitle of proceedings
Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015. Proceedings, Part IIUsage metrics
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