  Deep Learning has been very successful in many application domains. However,
its usefulness in the context of network intrusion detection has not been
systematically investigated. In this paper, we report a case study on using
deep learning for both supervised network intrusion detection and unsupervised
network anomaly detection. We show that Deep Neural Networks (DNNs) can
outperform other machine learning based intrusion detection systems, while
being robust in the presence of dynamic IP addresses. We also show that
Autoencoders can be effective for network anomaly detection.
