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.
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
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 Infosec Skills Cybersecurity Data Science learning path.
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