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

Authors:
Xu-Sheng Gan;Jing-Shun Duanmu;Jia-Fu Wang;Wei Cong
Affiliations:
XiJing College, Shaanxi, Xi'an 710123, China;Equipment Management and Safety Engineering College, Air Force Engineering University, China;Engineering College, Air Force Engineering University, China;Engineering College, Air Force Engineering University, China
Venue:
Knowledge-Based Systems
Year:
2013

Citing 11
Cited 0

Intrusion detection

Intrusion detection
A Neural Network Component for an Intrusion Detection System

SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
Intrusion Detection

Intrusion Detection
Core Vector Machines: Fast SVM Training on Very Large Data Sets

The Journal of Machine Learning Research
New Support Vector Algorithms

Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research
Simpler core vector machines with enclosing balls

Proceedings of the 24th international conference on Machine learning
Trust Region Newton Method for Logistic Regression

The Journal of Machine Learning Research
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
An immunity-based technique to characterize intrusions in computernetworks

IEEE Transactions on Evolutionary Computation
Intrusion detection techniques and approaches

Computer Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.