  Network intrusion is a well-studied area of cyber security. Current machine
learning-based network intrusion detection systems (NIDSs) monitor network data
and the patterns within those data but at the cost of presenting significant
issues in terms of privacy violations which may threaten end-user privacy.
Therefore, to mitigate risk and preserve a balance between security and
privacy, it is imperative to protect user privacy with respect to intrusion
data. Moreover, cost is a driver of a machine learning-based NIDS because such
systems are increasingly being deployed on resource-limited edge devices. To
solve these issues, in this paper we propose a NIDS called PCC-LSM-NIDS that is
composed of a Pearson Correlation Coefficient (PCC) based feature selection
algorithm and a Least Square Method (LSM) based privacy-preserving algorithm to
achieve low-cost intrusion detection while providing privacy preservation for
sensitive data. The proposed PCC-LSM-NIDS is tested on the benchmark intrusion
database UNSW-NB15, using five popular classifiers. The experimental results
show that the proposed PCC-LSM-NIDS offers advantages in terms of less
computational time, while offering an appropriate degree of privacy protection.
