Support vector data description with kernel density estimation (SVDD-KDE) control chart for network intrusion monitoring.

ABSTRACT: Multivariate control charts have been applied in many sectors. One of the sectors that employ this method is network intrusion detection. However, the issue arises when the conventional control chart faces difficulty monitoring the network-traffic data that do not follow a normal distribution as required. Consequently, more false alarms will be found when inspecting network traffic data. To settle this problem, support vector data description (SVDD) is suggested. The control chart based on the SVDD distance can be applied for the non-normal distribution, even the unknown distributions. Kernel density estimation (KDE) is the nonparametric approach that can be applied in estimating the control limit of the non-parametric control charts. Based on these facts, a multivariate chart based on the

SUBMITTER: Ahsan M 

PROVIDER: S-EPMC10628185 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature