LuNet: A Deep Neural Network for Network Intrusion Detection
  Network attack is a significant security issue for modern society. From small
mobile devices to large cloud platforms, almost all computing products, used in
our daily life, are networked and potentially under the threat of network
intrusion. With the fast-growing network users, network intrusions become more
and more frequent, volatile and advanced. Being able to capture intrusions in
time for such a large scale network is critical and very challenging. To this
end, the machine learning (or AI) based network intrusion detection (NID), due
to its intelligent capability, has drawn increasing attention in recent years.
Compared to the traditional signature-based approaches, the AI-based solutions
are more capable of detecting variants of advanced network attacks. However,
the high detection rate achieved by the existing designs is usually accompanied
by a high rate of false alarms, which may significantly discount the overall
effectiveness of the intrusion detection system. In this paper, we consider the
existence of spatial and temporal features in the network traffic data and
propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the
convolutional neural network (CNN) and the recurrent neural network (RNN) learn
input traffic data in sync with a gradually increasing granularity such that
both spatial and temporal features of the data can be effectively extracted.
Our experiments on two network traffic datasets show that compared to the
state-of-the-art network intrusion detection techniques, LuNet not only offers
a high level of detection capability but also has a much low rate of false
positive-alarm.
