  Network security applications, including intrusion detection systems of deep
neural networks, are increasing rapidly to make detection task of anomaly
activities more accurate and robust. With the rapid increase of using DNN and
the volume of data traveling through systems, different growing types of
adversarial attacks to defeat them create a severe challenge. In this paper, we
focus on investigating the effectiveness of different evasion attacks and how
to train a resilience deep learning-based IDS using different Neural networks,
e.g., convolutional neural networks (CNN) and recurrent neural networks (RNN).
We use the min-max approach to formulate the problem of training robust IDS
against adversarial examples using two benchmark datasets. Our experiments on
different deep learning algorithms and different benchmark datasets demonstrate
that defense using an adversarial training-based min-max approach improves the
robustness against the five well-known adversarial attack methods.
