_:n70839b02516e4a018ac6e060ab783b6exb13
<http://schema.org/description>
"Network intrusion is a well-studied area of cyber security. Current machine\nlearning-based network intrusion detection systems (NIDSs) monitor network data\nand the patterns within those data but at the cost of presenting significant\nissues in terms of privacy violations which may threaten end-user privacy.\nTherefore, to mitigate risk and preserve a balance between security and\nprivacy, it is imperative to protect user privacy with respect to intrusion\ndata. Moreover, cost is a driver of a machine learning-based NIDS because such\nsystems are increasingly being deployed on resource-limited edge devices. To\nsolve these issues, in this paper we propose a NIDS called PCC-LSM-NIDS that is\ncomposed of a Pearson Correlation Coefficient (PCC) based feature selection\nalgorithm and a Least Square Method (LSM) based privacy-preserving algorithm to\nachieve low-cost intrusion detection while providing privacy preservation for\nsensitive data. The proposed PCC-LSM-NIDS is tested on the benchmark intrusion\ndatabase UNSW-NB15, using five popular classifiers. The experimental results\nshow that the proposed PCC-LSM-NIDS offers advantages in terms of less\ncomputational time, while offering an appropriate degree of privacy protection."
<https://cdnjs.deepai.org/profile/wencheng-yang>