Anomaly intrusion detection based on PLS feature extraction and core vector machine

作者:

Highlights:

摘要

To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Partial Least Square (PLS) feature extraction and Core Vector Machine (CVM) algorithms. Principal elements are firstly extracted from the data set using the feature extraction of PLS algorithm to construct the feature set, and then the anomaly intrusion detection model for the feature set is established by virtue of the speediness superiority of CVM algorithm in processing large-scale sample data. Finally, anomaly intrusion actions are checked and judged using this model. Experiments based on KDD99 data set verify the feasibility and validity of the combined algorithm.

论文关键词:Core vector machine,Partial least square,Feature extraction,Anomaly intrusion detection,Support Vector Machine

论文评审过程:Received 25 April 2012, Revised 13 September 2012, Accepted 13 September 2012, Available online 22 October 2012.

论文官网地址:https://doi.org/10.1016/j.knosys.2012.09.004