• Classroom
  • Online, Instructor-Led
  • Online, Self-Paced

Focuses on applying machine learning techniques to cybersecurity, and includes labs to be done independently, as well as an overview of the latest machine learning algorithms and their application to cyber. A brief overview of which techniques should be applied to particular cyber problems will be provided, and the course culminates in students researching the latest applications of Machine learning to cyber, allowing the students to each develop a niche of expertise in that specific subtopic. As such, the students should be increasingly employable in their area of cyber expertise by industries searching for solutions to their cyber problem space.

Learning Objectives

Apply relevant machine learning techniques to problems across various areas within the cyber security domain that include malware analysis, memory forensics, network traffic analysis, and vulnerability research. Use multiple approaches to transform cyber security data (and metadata) into a mathematical representation (i.e., vector) suitable for ingesting by Machine Learning algorithms.. Develop methods to select, implement, and modify appropriate algorithms that are suitable for the cyber security application of interest.. Research and investigate late-breaking methods in ML and their application to cyber. Ideally, each student will develop the beginnings of their unique expertise in a subtopic of their choosing for ML-cyber applications. Understand cyber resilience, and how to survive or outwit a threat.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):