  Growing number of network devices and services have led to increasing demand
for protective measures as hackers launch attacks to paralyze or steal
information from victim systems. Intrusion Detection System (IDS) is one of the
essential elements of network perimeter security which detects the attacks by
inspecting network traffic packets or operating system logs. While existing
works demonstrated effectiveness of various machine learning techniques, only
few of them utilized the time-series information of network traffic data. Also,
categorical information has not been included in neural network based
approaches. In this paper, we propose network intrusion detection models based
on sequential information using long short-term memory (LSTM) network and
categorical information using the embedding technique. We have experimented the
models with UNSW-NB15, which is a comprehensive network traffic dataset. The
experiment results confirm that the proposed method improve the performance,
observing binary classification accuracy of 99.72\%.
