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

作者：Gan Xu-sheng^*; Duanmu Jing-shun; Wang Jia-fu; Cong Wei

来源：Knowledge-Based Systems, 2013, 40: 1-6.

DOI：10.1016/j.knosys.2012.09.004

• Core vector machine
• Partial least square
• Feature extraction
• Anomaly intrusion detection
• Support Vector Machine

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.

• 出版日期2013-3
• 单位中国人民解放军空军工程大学; 西京学院