Eigenspace analysis for threat detection in social networks

The problem of detecting a small, anomalous subgraph within a large background network is important and applicable to many fields. The non-Euclidean nature of graph data, however, complicates the application of classical detection theory in this context. A recent statistical framework for anomalous...

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Bibliographic Details
Main Authors: Miller, B. A., Beard, M. S., Bliss, N. T.
Format: Conference Proceeding
Language:English
Subjects:
Eigenvalues and eigenfunctions
Generators
Image edge detection
network modularity
Network topology
Noise
Noise measurement
signal detection theory
Social network services
Subgraph detection
threat network detection
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Summary:The problem of detecting a small, anomalous subgraph within a large background network is important and applicable to many fields. The non-Euclidean nature of graph data, however, complicates the application of classical detection theory in this context. A recent statistical framework for anomalous subgraph detection uses spectral properties of a graph's modularity matrix to determine the presence of an anomaly. In this paper, this detection framework and the related algorithms are applied to data focused on a specific application: detection of a threat subgraph embedded in a social network. The results presented use data created to simulate threat activity among noisy interactions. The detectability of the threat subgraph and its separability from the noise is analyzed under a variety of background conditions in both static and dynamic scenarios.