  The increase of cyber attacks in both the numbers and varieties in recent
years demands to build a more sophisticated network intrusion detection system
(NIDS). These NIDS perform better when they can monitor all the traffic
traversing through the network like when being deployed on a Software-Defined
Network (SDN). Because of the inability to detect zero-day attacks,
signature-based NIDS which were traditionally used for detecting malicious
traffic are beginning to get replaced by anomaly-based NIDS built on neural
networks. However, recently it has been shown that such NIDS have their own
drawback namely being vulnerable to the adversarial example attack. Moreover,
they were mostly evaluated on the old datasets which don't represent the
variety of attacks network systems might face these days. In this paper, we
present Reconstruction from Partial Observation (RePO) as a new mechanism to
build an NIDS with the help of denoising autoencoders capable of detecting
different types of network attacks in a low false alert setting with an
enhanced robustness against adversarial example attack. Our evaluation
conducted on a dataset with a variety of network attacks shows denoising
autoencoders can improve detection of malicious traffic by up to 29% in a
normal setting and by up to 45% in an adversarial setting compared to other
recently proposed anomaly detectors.
