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Machine Learning in Cybersecurity: A Guide

Technical Report
This report suggests seven key questions that managers and decision makers should ask about machine learning tools to effectively use those tools to solve cybersecurity problems.
Publisher

Software Engineering Institute

CMU/SEI Report Number
CMU/SEI-2019-TR-005
DOI (Digital Object Identifier)
10.1184/R1/12363089.v1

Abstract

This report lists relevant questions that decision makers should ask of machine-learning practitioners before employing machine learning (ML) or artificial intelligence (AI) solutions in the area of cybersecurity. Like any tool, ML tools should be a good fit for the purpose they are intended to achieve. The questions in this report will improve decision makers’ ability to select an appropriate ML tool and make it a good fit to address their cybersecurity topic of interest. In addition, the report outlines the type of information that good answers to the questions should contain. This report covers the following questions:

  1. What is your topic of interest?
  2. What information will help you address the topic of interest?
  3. How do you anticipate that an ML tool will address the topic of interest?
  4. How will you protect the ML system against attacks in an adversarial, cybersecurity environment?
  5. How will you find and mitigate unintended outputs and effects?
  6. Can you evaluate the ML tool adequately, accounting for errors?
  7. What alternative tools have you considered? What are the advantages and disadvantages of each one?

Cite This Technical Report

Spring, J., Fallon, J., Galyardt, A., Horneman, A., Metcalf, L., & Stoner, E. (2019, September 5). Machine Learning in Cybersecurity: A Guide. (Technical Report CMU/SEI-2019-TR-005). Retrieved May 24, 2024, from https://doi.org/10.1184/R1/12363089.v1.

@techreport{spring_2019,
author={Spring, Jonathan and Fallon, Joshua and Galyardt, April and Horneman, Angela and Metcalf, Leigh and Stoner, Ed},
title={Machine Learning in Cybersecurity: A Guide},
month={Sep},
year={2019},
number={CMU/SEI-2019-TR-005},
howpublished={Carnegie Mellon University, Software Engineering Institute's Digital Library},
url={https://doi.org/10.1184/R1/12363089.v1},
note={Accessed: 2024-May-24}
}

Spring, Jonathan, Joshua Fallon, April Galyardt, Angela Horneman, Leigh Metcalf, and Ed Stoner. "Machine Learning in Cybersecurity: A Guide." (CMU/SEI-2019-TR-005). Carnegie Mellon University, Software Engineering Institute's Digital Library. Software Engineering Institute, September 5, 2019. https://doi.org/10.1184/R1/12363089.v1.

J. Spring, J. Fallon, A. Galyardt, A. Horneman, L. Metcalf, and E. Stoner, "Machine Learning in Cybersecurity: A Guide," Carnegie Mellon University, Software Engineering Institute's Digital Library. Software Engineering Institute, Technical Report CMU/SEI-2019-TR-005, 5-Sep-2019 [Online]. Available: https://doi.org/10.1184/R1/12363089.v1. [Accessed: 24-May-2024].

Spring, Jonathan, Joshua Fallon, April Galyardt, Angela Horneman, Leigh Metcalf, and Ed Stoner. "Machine Learning in Cybersecurity: A Guide." (Technical Report CMU/SEI-2019-TR-005). Carnegie Mellon University, Software Engineering Institute's Digital Library, Software Engineering Institute, 5 Sep. 2019. https://doi.org/10.1184/R1/12363089.v1. Accessed 24 May. 2024.

Spring, Jonathan; Fallon, Joshua; Galyardt, April; Horneman, Angela; Metcalf, Leigh; & Stoner, Ed. Machine Learning in Cybersecurity: A Guide. CMU/SEI-2019-TR-005. Software Engineering Institute. 2019. https://doi.org/10.1184/R1/12363089.v1