Conference Proceedings

An Embedding Scheme for Detecting Anomalous Block Structured Graphs

Lida Rashidi, Sutharshan Rajasegarar, Christopher Leckie, T Cao (ed.), EP Lim (ed.), ZH Zhou (ed.), TB Ho (ed.), D Cheung (ed.), H Motoda (ed.)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | SPRINGER-VERLAG BERLIN | Published : 2015


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 p..

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