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Anomaly Detection in Large-Scale Networks With Latent Space Models.
Wesley Lee1, Tyler H McCormick2, Joshua Neil3
1Department of Statistics, University of Washington, Seattle, DC.
Summary
This study introduces a novel real-time anomaly detection method for directed network activity. The approach efficiently identifies unusual patterns, significantly improving detection rates for network security threats.
Area of Science:
- Computer Science
- Network Security
- Data Mining
Background:
- Large, sparse networks present challenges for real-time anomaly detection.
- Existing methods struggle with dynamic network activity and computational complexity.
Purpose of the Study:
- To develop an efficient real-time anomaly detection method for directed activity on large, sparse networks.
- To improve the detection of network security threats by modeling latent network dynamics.
Main Methods:
- A dynamic logistic model incorporating sender/receiver latent factors and popularity scores was developed.
- Latent nodal attributes were estimated using a variational Bayesian approach with time-varying capabilities.
- A case-control approximation was employed to reduce computational complexity from O(N^2) to O(E).
Main Results:
- The algorithm was tested on enterprise network event records from over 25,000 computers.
- The method successfully identified a red team attack.
- Detection rates were significantly improved compared to models lacking latent interaction terms.
Conclusions:
- The developed anomaly detection method is effective for large, sparse networks.
- The inclusion of latent factors and a case-control approximation enhances detection efficiency and accuracy.
- This approach offers a promising solution for real-time network security monitoring.
