  A rising number of botnet families have been successfully detected using deep
learning architectures. While the variety of attacks increases, these
architectures should become more robust against attacks. They have been proven
to be very sensitive to small but well constructed perturbations in the input.
Botnet detection requires extremely low false-positive rates (FPR), which are
not commonly attainable in contemporary deep learning. Attackers try to
increase the FPRs by making poisoned samples. The majority of recent research
has focused on the use of model loss functions to build adversarial examples
and robust models. In this paper, two LSTM-based classification algorithms for
botnet classification with an accuracy higher than 98% are presented. Then, the
adversarial attack is proposed, which reduces the accuracy to about 30%. Then,
by examining the methods for computing the uncertainty, the defense method is
proposed to increase the accuracy to about 70%. By using the deep ensemble and
stochastic weight averaging quantification methods it has been investigated the
uncertainty of the accuracy in the proposed methods.
