Machine Learning for Pentesting Course

Employ machine learning to take your penetration testing to the next level by unlocking information and access thought impenetrable before.

18 minutes

Course description

This course will take your penetration testing to the next level by enriching your toolkit with machine learning-based methods. You will learn how to perform neural network-assisted fuzzing, how to break CAPTCHA systems and how to deanonymize the browsing activity of Tor users. You will unlock information and access thought impenetrable before.

Syllabus

Deanonymizing Tor

Video - 00:06:00
Neural Network Assisted Fuzzing

Video - 00:04:00
CAPTCHA Breaking

Video - 00:06:00
Overview

Video - 00:01:00

Meet the author

Emmanuel Tsukerman

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.

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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

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