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Course 24 - Machine Learning for Red Team Hackers | Episode 2: Building and Implementing Evolutionary Testing Tools
In this lesson, you’ll learn about:
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy
- What fuzzing is and why it’s a powerful technique for discovering software vulnerabilities
- The difference between basic randomized fuzzing and more advanced, coverage-guided approaches
- How code coverage helps measure which parts of a program are exercised during testing
- Why naive random input generation is inefficient for complex formats like PDFs
- The concept of mutation-based fuzzing, including byte-level modifications such as insertion, deletion, swapping, and randomization
- How evolutionary fuzzing applies principles from genetic algorithms to improve input effectiveness
- The role of a fitness function in selecting high-value test cases
- How recombination and mutation evolve a population of inputs to reach deeper code paths
- How professional tools like American Fuzzy Lop instrument compiled programs to detect unique crashes and segmentation faults
- Why fuzzing is critical for secure software development and proactive vulnerability discovery
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy
View all episodes
By CyberCode AcademyIn this lesson, you’ll learn about:
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy
- What fuzzing is and why it’s a powerful technique for discovering software vulnerabilities
- The difference between basic randomized fuzzing and more advanced, coverage-guided approaches
- How code coverage helps measure which parts of a program are exercised during testing
- Why naive random input generation is inefficient for complex formats like PDFs
- The concept of mutation-based fuzzing, including byte-level modifications such as insertion, deletion, swapping, and randomization
- How evolutionary fuzzing applies principles from genetic algorithms to improve input effectiveness
- The role of a fitness function in selecting high-value test cases
- How recombination and mutation evolve a population of inputs to reach deeper code paths
- How professional tools like American Fuzzy Lop instrument compiled programs to detect unique crashes and segmentation faults
- Why fuzzing is critical for secure software development and proactive vulnerability discovery
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy