generative adversarial network (GAN). We apply these algorithms to two well-known data sets, NSL-KDD and UNSW-NB15, and show that the perturbations created by these techniques are able to fool eleven different machine learning models, plus an ensemble model. For our baseline adversarial example generation method, we use a Monte Carlo (MC) simulation for random generation of perturbations.

The paper is organized as follows. In Section \[sec:Pre\], we give an overview of the field of machine learning. We set the notation and establish the state of the art taxonomy therein. In Section \[sec:Lit\], we review the most relevant and up to date literature that deals with adversarial machine learning. Section \[sec:Meth\] describes the methodology used to generate adversarial examples. We explain the intuition as well as the technical details of our chosen perturbation methods. In Section \[sec:Res\], we apply our evolutionary computation and deep learning methods to the two data sets mentioned above, and we record the results while contrasting them with the baseline perturbation method. We also discuss our findings and distinguish between the machine learning models that are highly sensitive and the ones that show some type of robustness. We conclude our work in Section \[sec:Conc\] and pose some 