  One challenge for building a secure network communication environment is how
to effectively detect and prevent malicious network behaviours. The abnormal
network activities threaten users' privacy and potentially damage the function
and infrastructure of the whole network. To address this problem, the network
intrusion detection system (NIDS) has been used. By continuously monitoring
network activities, the system can timely identify attacks and prompt
counter-attack actions. NIDS has been evolving over years. The
current-generation NIDS incorporates machine learning (ML) as the core
technology in order to improve the detection performance on novel attacks.
However, the high detection rate achieved by a traditional ML-based detection
method is often accompanied by large false-alarms, which greatly affects its
overall performance. In this paper, we propose a deep neural network, Pelican,
that is built upon specially-designed residual blocks. We evaluated Pelican on
two network traffic datasets, NSL-KDD and UNSW-NB15. Our experiments show that
Pelican can achieve a high attack detection performance while keeping a much
low false alarm rate when compared with a set of up-to-date machine learning
based designs.
