  Network intrusion detection is one of the most important issues in the field
of cyber security, and various machine learning techniques have been applied to
build intrusion detection systems. However, since the number of features to
describe the network connections is often large, where some features are
redundant or noisy, feature selection is necessary in such scenarios, which can
both improve the efficiency and accuracy. Recently, some researchers focus on
using multi-objective evolutionary algorithms (MOEAs) to select features. But
usually, they only consider the number of features and classification accuracy
as the objectives, resulting in unsatisfactory performance on a critical
metric, detection rate. This will lead to the missing of many real attacks and
bring huge losses to the network system. In this paper, we propose DR-MOFS to
model the feature selection problem in network intrusion detection as a
three-objective optimization problem, where the number of features, accuracy
and detection rate are optimized simultaneously, and use MOEAs to solve it.
Experiments on two popular network intrusion detection datasets NSL-KDD and
UNSW-NB15 show that in most cases the proposed method can outperform previous
methods, i.e., lead to fewer features, higher accuracy and detection rate.
