Learning Path

Machine Learning for Red Team Hackers

    Syllabus

  • Deepfake Course — 00:26:05
    • K0119, K0238, K0309, K0342, S0051

      This course helps you understand the implications of the deepfake technology, setup an environment for performing deepfakes and then create a deepfake video.

  • Evading Machine Learning Malware Classifiers Course — 00:21:04
    • K0119, K0238, K0309, K0342, K0430, K0480, S0051

      In this course, you’ll learn how to modify malware so it is able to get past neural network and gradient boosting machine learning malware classifiers.

  • Hacking CAPTCHA Systems Course — 00:56:51
    • K0119, K0177, K0238, K0309, K0342, S0051

      Learn how to automatically break through a CAPTCHA system in this course.

  • Hacking Machine Learning Course — 00:14:25
    • K0119, K0238, K0309, K0342, S0051

      Learn how to poison, backdoor and steal machine learning models in this course.

  • Adversarial Machine Learning Course — 00:33:02
    • Learn how to perform white-box and black-box attacks on machine learning classifiers in this course.

  • Smart Fuzzing Course — 00:49:14
    • K0119, K0177, K0238, K0309, K0342, K0529, S0051, S0081, S0130

      In this course, you’ll write a custom evolutionary fuzzer that employs machine learning to fuzz a target. You’ll also learn the machine learning behind, as well as how to use, AFL.

  • Introduction to Machine Learning for Red Team Hackers Course — 00:03:32
    • This course provides a brief overview of the learning path structure, as well as introduction to the code base you’ll use.

  • Machine Learning for Red Team Hackers Skill Assessment Assessment — 00:17:30

Syllabus

What you will learn

Learn the ins and outs of hacking machine learning. The learning path covers topics such as hacking a CAPTCHA system, fuzzing a target, evading malware detection and attacking machine learning systems. You will also learn about deepfakes and how to perform backdoor attacks on machine learning.

Wistia video thumbnail

Meet the author

Dr. Tsukerman graduated from Stanford University and UC Berkeley. In 2017, his machine-learning-based anti-ransomware product won Top 10 Ransomware Products by PC Magazine. In 2018, he designed a machine-learning-based malware detection system for Palo Alto Network’s WildFire service (over 30,000 customers).

In 2019, Dr. Tsukerman authored the Machine Learning for Cybersecurity Cookbook and launched the Infosec Skills Cybersecurity Data Science Learning Path.

The details

Learning path insights

How to claim CPEs

Should you complete this learning path, you’ll be able to download a certificate of completion. Use this to claim your CPEs or CPUs.

Associated NICE Work Roles

All Infosec training maps directly to the NICE Workforce Framework for Cybersecurity to guide you from beginner to expert across 52 Work Roles.

  • All-Source Analyst
  • Mission Assessment Specialist
  • Exploitation Analyst

No software. No set up. Unlimited access.

Skip the server racks and spin up a realistic environment with one click. lnfosec Skills cyber ranges require no additional software, hardware or server space so your team can spend less time configuring environments and more time learning. Unlimited cyber range access is included in every lnfosec Skills subscription so your team can skill up however they learn best.

Plans & pricing

  • Infosec Skills Personal

    $299 / year

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    • 190+ role-guided learning paths and assessments (e.g., Incident Response)
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This has been utilized to bridge the skills gap across our cyber team and to aid them as they prepare for their various certifications. It also has provided a nice learning foundation for our various cyber team members to utilize as we continue to find ways for cross-utilization with operations while minimizing the downtime needed to ensure everyone’s knowledge is the same.

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