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Introduction to Machine Learning in Cybersecurity

Published by Pearson

Beginner content levelBeginner
  • Hands on machine learning for cybersecurity
  • Learn Large Language Models
  • Understand theory and practical applications

This is an overview of how machine learning impacts cybersecurity. The first 60% of the course is an introduction to machine learning covering a wide range of techniques and algorithms. Then the course will discuss how those algorithms can be applied to machine learning both defensively (i.e. in anti-virus, SIEM, IDS/IPS, etc.) and offensively (in cyber warfare).

Machine learning is fast becoming an integral aspect of all areas of the computer industry. And cybersecurity is already moving to embrace machine learning. It is important that all cybersecurity professionals, regardless of their specific specialty, have a working knowledge of machine learning's impact on cybersecurity.

What you’ll learn and how you can apply it

  • Understand the fundamentals of machine learning
  • Know what algorithms are useful for what problems
  • Recognize how machine learning can enhance cyber defense
  • Explore how machine learning can be integrated into cyber warfare.

This live event is for you because...

  • IT Personnel (programmers, network admins, etc.) can gain an understanding of machine learning without having a background in ML/AI.
  • The impact of machine learning on IT and cybersecurity is so significant that all professionals in these fields need to have at least a working knowledge.
  • It is a prerequisite for more advanced training.

Prerequisites

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1: Introductory concepts (Length: 45 mins)

  • What is machine learning (and what it's not!)
  • Supervised v Unsupervised
  • Basic concepts (training sets, over training, etc.)

Break: 10 mins

Segment 2: Clustering algorithms (Length: 45 mins)

  • k-nearest neighbor algorithm
  • naive Bayes
  • k-means
  • Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  • Self-organizing map

Break: 10 mins

Segment 3: Neural Networks (Length: 45 mins)

  • Structure
  • Feed forward networks
  • Recurrent networks
  • Convolutional Neural Networks
  • Long/Short term memory
  • Hopfield Network
  • Generative Adversarial Network
  • Boltzman machines

Break: 10 mins

Segment 4: ML and Cybersecurity (Length: 30 mins)

  • Integrating ML into defenses
  • Integrating ML into offensive

Course wrap-up and next steps

Your Instructor

  • Dr. Chuck Easttom

    Dr. Chuck Easttom is the author of 42 books, including several on computer security, forensics, and cryptography. He is also an inventor with 26 patents and the author of over 70 research papers. He holds a Ph.D. in computer science, a Ph.D. in Nanotechnology, a Doctor of Science in Cybersecurity, and four master’s degrees (one in applied computer science, one in education, one in Strategic and Defense studies, and one in systems engineering). He also holds 80 industry certifications. He is a senior member of both the IEEE and the ACM. He is also a Distinguished Speaker of the ACM and a Distinguished Visitor of the IEEE. Dr. Easttom is currently an adjunct professor at Georgetown University and Vanderbilt University.

Skill covered

Machine Learning