Edinburgh Napier University

  The ability to detect metamorphic malware has generated significant research interest over recent years, particularly given its proliferation on mobile devices. Such malware is particularly hard to detect via signature-based intrusion detection systems due to its ability to change its code over time. This article describes a novel framework which generates sets of potential mutants and then uses them as training data to inform the development of improved detection methods (either in two separate phases or in an adversarial learning setting). We outline a method to implement the mutant generation step using an evolutionary algorithm, providing preliminary results that show that the concept is viable as the first steps towards instantiation of the full framework.