1. Anomaly Detection in Large-Scale Networks With Latent Space Models.
- Author
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Lee, Wesley, McCormick, Tyler H., Neil, Joshua, Sodja, Cole, and Cui, Yanran
- Subjects
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ANOMALY detection (Computer security) , *COMPUTATIONAL complexity , *RECORD collecting , *TEAMS in the workplace , *DYNAMIC models , *LATENT semantic analysis , *APPROXIMATION algorithms - Abstract
We develop a real-time anomaly detection method for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms for sender- and receiver-specific latent factors in addition to sender- and receiver-specific popularity scores; deviations from this underlying model constitute potential anomalies. Latent nodal attributes are estimated via a variational Bayesian approach and may change over time, representing natural shifts in network activity. Estimation is augmented with a case-control approximation to take advantage of the sparsity of the network and reduces computational complexity from O (N 2) to O(E), where N is the number of nodes and E is the number of observed edges. We run our algorithm on network event records collected from an enterprise network of over 25,000 computers and are able to identify a red team attack with half the detection rate required of the model without latent interaction terms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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