  A myriad of recent literary works has leveraged generative adversarial
networks (GANs) to generate unseen evasion samples. The purpose is to annex the
generated data with the original train set for adversarial training to improve
the detection performance of machine learning (ML) classifiers. The quality of
generated adversarial samples relies on the adequacy of training data samples.
However, in low data regimes like medical diagnostic imaging and cybersecurity,
the anomaly samples are scarce in number. This paper proposes a novel GAN
design called Evasion Generative Adversarial Network (EVAGAN) that is more
suitable for low data regime problems that use oversampling for detection
improvement of ML classifiers. EVAGAN not only can generate evasion samples,
but its discriminator can act as an evasion-aware classifier. We have
considered Auxiliary Classifier GAN (ACGAN) as a benchmark to evaluate the
performance of EVAGAN on cybersecurity (ISCX-2014, CIC-2017 and CIC2018) botnet
and computer vision (MNIST) datasets. We demonstrate that EVAGAN outperforms
ACGAN for unbalanced datasets with respect to detection performance, training
stability and time complexity. EVAGAN's generator quickly learns to generate
the low sample class and hardens its discriminator simultaneously. In contrast
to ML classifiers that require security hardening after being adversarially
trained by GAN-generated data, EVAGAN renders it needless. The experimental
analysis proves that EVAGAN is an efficient evasion hardened model for low data
regimes for the selected cybersecurity and computer vision datasets. Code will
be available at HTTPS://www.github.com/rhr407/EVAGAN.
