An attempt to practically apply machine learning to insider threats.
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README.md

A Practical Application of Machine Learning-Based Classification Techniques to Proactively Identify Insider Threats


Motivation and Introduction

Insider threats are on the rise. Current commercial software can monitor, log, and prevent access to designated files and directories.However, it remains difficult to predict and prevent unauthorized insider usage. Due to the gaps in research in the area, the focus of this study is to more accurately predict insider threats in a server environment.

Presented At

  • The Arizona-Nevada Academy of Science - April 1, 2017
  • The Northern Arizona University Undergraduate Symposium - April 28, 2017

Acknowledgements

Faculty Advisor - James A. Subach, Ph. D
Academic Credit Advisor - Nancy L. Jensen
Code Help - Ian Harvey on Stack Overflow
Data Sources - Purdue University
Poster Template - Felix Breuer
The NAU Cyber Security Team
The Embry-Riddle [Ethical] Hackers Club
Amazon Web Services
Font Awesome
GitHub