Abstract
In the present era, cyberspace is growing tremendously and the intrusion
detection system (IDS) plays a key role in it to ensure information
security. The IDS, which works in network and host level, should be
capable of identifying various malicious attacks. The job of
network-based IDS is to differentiate between normal and malicious
traffic data and raise an alert in case of an attack. Apart from the
traditional signature and anomaly-based approaches, many researchers
have employed various deep learning (DL) techniques for detecting
intrusion as DL models are capable of extracting salient features
automatically from the input data. The application of deep convolutional
neural network (DCNN), which is utilized quite often for solving
research problems in image processing and vision fields, is not explored
much for IDS. In this paper, a DCNN architecture for IDS which is
trained on KDDCUP 99 data set is proposed. This work also shows that the
DCNN-IDS model performs superior when compared with other existing
works.