Malware Detection via Machine Learning Course

Gain a practical understanding of the most successful techniques used by Cybersecurity Data Science experts for the crafting of malware classifiers.

43 minutes

Course description

You will learn how to perform static and dynamic analysis manually as well as automatically, how to build static and dynamic classifiers and how to troubleshoot imbalanced data and satisfy the common false positive constraint.

Syllabus

Training a Dynamic Malware Classifier

Video - 00:03:00
Malware Dynamic Analysis

Video - 00:05:00
Tackling False Positive Constraints

Video - 00:07:00
Tackling Class Imbalance

Video - 00:05:00
Training a Static Malware Detector

Video - 00:03:00
Selecting the Best N-grams

Video - 00:05:00
N-gram Features for Binary Files

Video - 00:04:00
Featurizing the PE Header

Video - 00:03:00
Understanding the PE Header

Video - 00:03:00
Malware Static Analysis

Video - 00:04:00
Overview

Video - 00:02:00

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
  • Target Developer

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