  Intrusion detection system (IDS) plays an essential role in computer networks
protecting computing resources and data from outside attacks. Recent IDS faces
challenges improving flexibility and efficiency of the IDS for unexpected and
unpredictable attacks. Deep neural network (DNN) is considered popularly for
complex systems to abstract features and learn as a machine learning technique.
In this paper, we propose a deep learning approach for developing the efficient
and flexible IDS using one-dimensional Convolutional Neural Network (1D-CNN).
Two-dimensional CNN methods have shown remarkable performance in detecting
objects of images in computer vision area. Meanwhile, the 1D-CNN can be used
for supervised learning on time-series data. We establish a machine learning
model based on the 1D-CNN by serializing Transmission Control Protocol/Internet
Protocol (TCP/IP) packets in a predetermined time range as an invasion Internet
traffic model for the IDS, where normal and abnormal network traffics are
categorized and labeled for supervised learning in the 1D-CNN. We evaluated our
model on UNSW\_NB15 IDS dataset to show the effectiveness of our method. For
comparison study in performance, machine learning-based Random Forest (RF) and
Support Vector Machine (SVM) models in addition to the 1D-CNN with various
network parameters and architecture are exploited. In each experiment, the
models are run up to 200 epochs with a learning rate in 0.0001 on imbalanced
and balanced data. 1D-CNN and its variant architectures have outperformed
compared to the classical machine learning classifiers. This is mainly due to
the reason that CNN has the capability to extract high-level feature
representations that represent the abstract form of low-level feature sets of
network traffic connections.
