Breadcrumb
  1. Training
  2. Education & Training Catalog
  3. Maryland Applied Graduate Engineering at the University of Maryland
  4. Machine Learning Techniques Applied to Cybersecurity

Machine Learning Techniques Applied to Cybersecurity

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.

Provider Information

More courses from this provider:
Contact Information

Heather Markle
Maryland Applied Graduate Engineering at the University of Maryland
2105 J.M. Patterson
College Park, MD 20742

Course Overview

Overall Proficiency Level
3 - Advanced
Course Prerequisites

N/A

Training Purpose
Functional Development
Skill Development
Specific Audience
All
Delivery Method
Classroom
Online, Instructor-Led
Online, Self-Paced
Course Location

4356 Stadium Drive
College Park, MD 20742

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

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):

Feedback

If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov(link sends email). Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.

Last Published Date:

You have been selected to participate in a brief survey about your experience today with National Initiative for Cybersecurity Careers and Studies.

Would you like to participate in our survey?

If you accept you will be leaving the National Initiative for Cybersecurity Careers and Studies website and going to a third party site.
That site may have different privacy, security and accessibility policies than the National Initiative for Cybersecurity Careers and Studies site.
National Initiative for Cybersecurity Careers and Studies does not endorse any commercial products, services, programs or content on the third party website.
Thank you for visiting our site. We hope your visit was informative and enjoyable.