Predicting User Roles from Computer Logs Using Recurrent Neural Networks

Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean Robinson

AAAI 2017 pp. 4993-4994

doi:10.1609/AAAI.V31I1.11069 /aaai/2017/tuor2017aaai-predicting/

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).

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Cite

Text

Tuor et al. "Predicting User Roles from Computer Logs Using Recurrent Neural Networks." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11069

Markdown

[Tuor et al. "Predicting User Roles from Computer Logs Using Recurrent Neural Networks." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/tuor2017aaai-predicting/) doi:10.1609/AAAI.V31I1.11069

BibTeX

@inproceedings{tuor2017aaai-predicting,
  title     = {{Predicting User Roles from Computer Logs Using Recurrent Neural Networks}},
  author    = {Tuor, Aaron and Kaplan, Samuel and Hutchinson, Brian and Nichols, Nicole and Robinson, Sean},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {4993-4994},
  doi       = {10.1609/AAAI.V31I1.11069},
  url       = {https://mlanthology.org/aaai/2017/tuor2017aaai-predicting/}
}