Mining and Generating Vulnerable Patterns for Security Testing

  • Anton Ibragimov

Student thesis: Master typesMaster in Computer Science Professional focus in Software engineering

Abstract

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.
Date of Award22 Jun 2018
Original languageEnglish
Awarding Institution
  • University of Namur
SupervisorPatrick HEYMANS (Supervisor) & GILLES PERROUIN (Co-Supervisor)

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