  Botnet detectors based on machine learning are potential targets for
adversarial evasion attacks. Several research works employ adversarial training
with samples generated from generative adversarial nets (GANs) to make the
botnet detectors adept at recognising adversarial evasions. However, the
synthetic evasions may not follow the original semantics of the input samples.
This paper proposes a novel GAN model leveraged with deep reinforcement
learning (DRL) to explore semantic aware samples and simultaneously harden its
detection. A DRL agent is used to attack the discriminator of the GAN that acts
as a botnet detector. The discriminator is trained on the crafted perturbations
by the agent during the GAN training, which helps the GAN generator converge
earlier than the case without DRL. We name this model RELEVAGAN, i.e. ["relive
a GAN" or deep REinforcement Learning-based Evasion Generative Adversarial
Network] because, with the help of DRL, it minimises the GAN's job by letting
its generator explore the evasion samples within the semantic limits. During
the GAN training, the attacks are conducted to adjust the discriminator weights
for learning crafted perturbations by the agent. RELEVAGAN does not require
adversarial training for the ML classifiers since it can act as an adversarial
semantic-aware botnet detection model. Code will be available at
https://github.com/rhr407/RELEVAGAN.
