Mining and Generating Vulnerable Patterns for Security Testing

Défense de mémoire d'Anton Ibragimov

Genetic programming is an evolutionary technique that improves individuals from a population in order to better fit the user’s needs. In our case, we apply this technique to create new vulnerabilities from an existing repository
mined from the Linux kernel. A first step was to create token sequences from this repository. Then, our genetic algorithm derives new vulnerable patterns according to a fitness function that rely on pattern frequency over the vulnerable
dataset. Our results indicate that genetic programming can indeed make vulnerabilities more robust over generations. Our patterns fall into two categories: generic patterns applicable to a large number of files and ones that can be used on a smaller set.

Contact : Isabelle Daelman - 4966 - isabelle.daelman@unamur.be
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