计算机工程与应用Issue(24)：86-90,95,6.DOI:10.3778/j.issn.1002-8331.1306-0242
混合PLS特征提取和CVM的异常入侵检测研究
Research on anomaly intrusion detection by hybrid Partial Least Square feature extrac-tion and Core Vector Machine
余文利 1方建文2
作者信息
- 1. 衢州职业技术学院 信息工程学院，浙江 衢州 324000
- 2. 衢州学院 电气与信息工程学院，浙江 衢州 324000
- 折叠
摘要
Abstract
To improve the efficiency of anomaly detecting intrusions, a hybrid model is proposed based on Partial Least Square(PLS)feature extraction and Core Vector Machine(CVM)algorithms. Principal elements are extracted from the intrusion data set by the feature extraction of PLS algorithm to establish the feature set, and then the anomaly intrusion detec-tion model for the feature set is constructed by virtue of speediness superiority of CVM algorithm in processing large-scale sample data. Anomaly intrusion actions are checked and judged using this model. Intrusion detection experiments based on KDD99 data set verify the feasibility and validity of the hybrid model.关键词
偏最小二乘/特征提取/核心向量机/异常入侵检测/支持向量机Key words
Partial Least Square(PLS)/feature extraction/Core Vector Machine(CVM)/anomaly intrusion detection/Support Vector Machine(SVM)分类
信息技术与安全科学引用本文复制引用
余文利,方建文..混合PLS特征提取和CVM的异常入侵检测研究[J].计算机工程与应用,2014,(24):86-90,95,6.
