Smart Fuzzing

In this course, you'll write a custom evolutionary fuzzer that employs machine learning to fuzz a target. You'll also learn the machine learning behind, as well as how to use, AFL.

6 videos  //  49 minutes of training

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Course description

The course begins by teaching the theory, as well as Python implementation, for naive and smart fuzzing, including the topics of code coverage, mutation and genetic algorithms for fuzzing. We will then write a custom evolutionary fuzzer that employs machine learning to fuzz a target, as well as understand the machine learning behind and usage of the famous fuzzer AFL. Finally, you will be given an assignment to hone your skills with fuzzing.

Course syllabus

What Is Fuzzing?Duration: 1:34

Brief overview of fuzzing for vulnerability detection.

Basic Fuzzing and Code CoverageDuration: 11:01

Write a toy fuzzer, understand the idea behind code coverage and instrument a program to obtain code coverage.

MutationDuration: 8:36

Understand how mutation is used in fuzzing.

How to Write Your Own Evolutionary FuzzerDuration: 19:36

Design and code an evolutionary fuzzer from scratch.

Fuzzing with AFLDuration: 7:45

Understand the machine learning behind, as well as use, AFL.

Assignment - FuzzMeDuration: 0:00

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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  • 190+ role-guided learning paths and assessments (e.g., Incident Response)
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