  Network intrusion detection (NID) is an essential defense strategy that is
used to discover the trace of suspicious user behaviour in large-scale
cyberspace, and machine learning (ML), due to its capability of automation and
intelligence, has been gradually adopted as a mainstream hunting method in
recent years. However, traditional ML based network intrusion detection systems
(NIDSs) are not effective to recognize unknown threats and their high detection
rate often comes with the cost of high false alarms, which leads to the problem
of alarm fatigue. To address the above problems, in this paper, we propose a
novel neural network based detection system, DualNet, which is constructed with
a general feature extraction stage and a crucial feature learning stage.
DualNet can rapidly reuse the spatial-temporal features in accordance with
their importance to facilitate the entire learning process and simultaneously
mitigate several optimization problems occurred in deep learning (DL). We
evaluate the DualNet on two benchmark cyber attack datasets, NSL-KDD and
UNSW-NB15. Our experiment shows that DualNet outperforms classical ML based
NIDSs and is more effective than existing DL methods for NID in terms of
accuracy, detection rate and false alarm rate.
