  Convolution Neural Network (ConvNet) offers a high potential to generalize
input data. It has been widely used in many application areas, such as visual
imagery, where comprehensive learning datasets are available and a ConvNet
model can be well trained and perform the required function effectively.
ConvNet can also be applied to network intrusion detection. However, the
currently available datasets related to the network intrusion are often
inadequate, which makes the ConvNet learning deficient, hence the trained model
is not competent in detecting unknown intrusions. In this paper, we propose a
ConvNet model using transfer learning for network intrusion detection. The
model consists of two concatenated ConvNets and is built on a two-stage
learning process: learning a base dataset and transferring the learned
knowledge to the learning of the target dataset. Our experiments on the NSL-KDD
dataset show that the proposed model can improve the detection accuracy not
only on the test dataset containing mostly known attacks (KDDTest+) but also on
the test dataset featuring many novel attacks (KDDTest-21) -- about 2.68\%
improvement on KDDTest+ and 22.02\% on KDDTest-21 can be achieved, as compared
to the traditional ConvNet model.
