Malware Detection via Machine Learning

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

11 videos  //  43 minutes of training

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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.

Course syllabus

OverviewDuration: 1:46

Malware Static AnalysisDuration: 4:05

Understanding the PE HeaderDuration: 3:13

Featurizing the PE HeaderDuration: 3:04

N-gram Features for Binary FilesDuration: 3:31

Selecting the Best N-gramsDuration: 4:55

Training a Static Malware DetectorDuration: 2:49

Tackling Class ImbalanceDuration: 5:15

Tackling False Positive ConstraintsDuration: 6:46

Malware Dynamic AnalysisDuration: 5:06

Training a Dynamic Malware ClassifierDuration: 2:48

Meet the author

Emmanuel Tsukerman

LinkedIn

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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