Predicting User Roles from Computer Logs Using Recurrent Neural Networks

Authors

  • Aaron Tuor Western Washington University
  • Samuel Kaplan Western Washington University
  • Brian Hutchinson Western Washington University
  • Nicole Nichols Pacific Northwest National Laboratory
  • Sean Robinson Pacific Northwest National Laboratory

DOI:

https://doi.org/10.1609/aaai.v31i1.11069

Keywords:

insider threat, neural network, streaming, anomaly detection

Abstract

Network and other computer administrators typically have access to a rich set of logs tracking actions by users. However, they often lack metadata such as user role, age, and gender that can provide valuable context for users' actions. Inferring user attributes automatically has wide ranging implications; among others, for customization (anticipating user needs and priorities), for managing resources (anticipating demand) and for security (interpreting anomalous behavior).

AAAI-2017 Proceedings Cover

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Published

2017-02-12

How to Cite

Tuor, A., Kaplan, S., Hutchinson, B., Nichols, N., & Robinson, S. (2017). Predicting User Roles from Computer Logs Using Recurrent Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11069

Issue

Vol. 31 No. 1 (2017): Thirty-First AAAI Conference on Artificial Intelligence

Section

Student Abstract Track