Anomaly Detection in Large Scale Networks with Latent Space Models
This addresses anomaly detection for enterprise network security, offering an incremental improvement over existing methods.
The paper tackles real-time anomaly detection in large, sparse directed networks by developing a dynamic logistic model with latent factors, reducing computational complexity from O(N^2) to O(E). It identifies a red team attack with half the detection rate compared to a baseline model without latent interactions.
We develop a real-time anomaly detection algorithm 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.