Machine Learning in Cybersecurity: A Guide
Abstract
Decision-makers should ask certain questions before employing machine-learning (ML) or artificial intelligence (AI) solutions and receive satisfactory answers. This document suggests important questions when employing ML or AI in cybersecurity and outlines what a satisfactory answer should contain. We focus on questions about quality and usefulness. The questions we discuss are: 1. What are you trying to find out? 2. What information is needed to answer the target question? 3. How do you anticipate that the ML/AI tool will address that question? 4. Is the design of the ML/AI tool robust to the well-known attacks against ML/AI in our adversarial, cybersecurity environment? 5. How can the input datas bias be managed? 6. Does the evaluation of the ML/AI tool properly account for well-known study design errors and biases?7. What alternative tools have you considered? What are the advantages and disadvantages of each?
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2019
- Accession Number
- AD1088210
Entities
People
- Jonathan M. Spring
- Joshua Fallon
- Leigh Metcalf
Organizations
- Carnegie Mellon University