  Nowadays, Deep Neural Networks (DNNs) report state-of-the-art results in many
machine learning areas, including intrusion detection. Nevertheless, recent
studies in computer vision have shown that DNNs can be vulnerable to
adversarial attacks that are capable of deceiving them into misclassification
by injecting specially crafted data. In security-critical areas, such attacks
can cause serious damage; therefore, in this paper, we examine the effect of
adversarial attacks on deep learning-based intrusion detection. In addition, we
investigate the effectiveness of adversarial training as a defense against such
attacks. Experimental results show that with sufficient distortion, adversarial
examples are able to mislead the detector and that the use of adversarial
training can improve the robustness of intrusion detection.

Cryptography and Security;Machine Learning