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Activity Number: 149 - Government Cybersecurity Research: Statistical Challenges and Opportunities
Type: Invited
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract #300194 Presentation
Title: Latent Feature Models for Network Link Prediction with Labelled Nodes
Author(s): Melissa Turcotte*
Companies: Los Alamos National Laboratory
Keywords: link prediction; cyber; anomaly detection; latent feature models; probabilistic matrix factorization

In cyber networks, relationships between entities, such as users interacting with computers, or system libraries and the corresponding processes that use them can provide key insights into adversary behaviour. Many cyber attack behaviours create new links between such entities - previously unobserved relationships. A probabilistic latent feature model is presented to predict the formation of new edges between entities in computer networks enabling anomaly scores to be assigned to new link formations over time. In particular, the Poisson matrix factorization model is extended to include known covariates about each entity or node. Results show that the including known covariates about each entity can improve predictive performance enhancing anomaly detection capabilities.

Authors who are presenting talks have a * after their name.

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