Applying Generative Adversarial Networks for the Generation of Adversarial Attacks Against Continuous Authentication

Abstract: Cybersecurity has been a hot topic over the past decades with lots of approaches being proposed to secure our private information. One of the emerging approaches in security is continuous authentication, in which the computer system is authenticating the user by monitoring the user behavior during the login session. Although the research of continuous authentication has got a significant achievement, the security of state-of-the-art continuous authentication systems is far from perfect. In this thesis, we explore the ability of classifiers used in continuous authentication and examine whether they can be bypassed by generated samples of user behavior from generative models. In our work, we considered four machine learning classifiers as the continuous authentication system: One-Class support vector machine, support vector machine, Gaussian mixture model and an artificial neural network. Furthermore, we considered three generative models used to mimic the user behavior: generative adversarial network, kernel density estimation generator, and MMSE-based generator. The considered classifiers and generative models were tested on two continuous authentication datasets. The result shows that generative adversarial networks achieved superior results with more than 50samples passing continuous authentication.