Evading Machine Learning Malware Classifiers Course

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.

21 minutes

Course description

The course begins by teaching the ideas behind ML-based malware detection and introduces the student to two such detectors, namely MalConv and EMBER. Next, you will learn how to easily modify samples using the LIEF and pefile libraries. You will then learn how to modify real ransomware samples so they are able to evade a ML-based malware classifier. Finally, you will solidify your learning via a hands-on assignment in evasion of a ML malware detector.

Syllabus

Assignment: EvadeMe

File
Assignment: EvadeMe video

Video - 00:01:00
Evading a Machine Learning Malware Classifier

Video - 00:10:00
Take a ransomware (jigsaw) that is detected by Ember and modify it so that it is no longer detected (but remains a ransomware).
Modifying PE Files the Easy Way

Video - 00:03:00
Tutorial on modifying PE files using LIEF and PE-file libraries.
Meet a Machine Learning Classifier

Video - 00:06:00
Overview and usage of Ember gradient-boosting based ML malware classifier.
Evading Machine Learning Malware Classifiers Overview

Video - 00:02:00
Overview of upcoming topics in the course.

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.

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