AbstractGenetic 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.
|Date of Award||22 Jun 2018|
|Supervisor||Patrick HEYMANS (Supervisor) & GILLES PERROUIN (Co-Supervisor)|