Intrusion Detection System Technique Based on BP-SVM

Author

Qiao, Pei-Li ; Chen, Shi-Feng

Author_Institution

Dept. Comput. Sci. & Technol., Harbin Univ. of Sci. & Technol., Harbin, China

fYear

2009

fDate

20-22 Sept. 2009

Firstpage

1

Lastpage

3

Abstract

Due to the fact that the detection of intrusion is inefficient and lacks intelligence in current intrusion detection system, this paper integrates BP neural network and support vector machine (SVM) based on the theory of neural network integration, applying fuzzy clustering technology to cluster data, choosing data from the cluster centre to train ensemble individuals, then selecting and integrating those individuals of significant diversity. The theoretical analysis and experimental results show that this ensemble method is efficient for detection rates and unknown attacks.

Keywords

backpropagation; fuzzy set theory; neural nets; security of data; support vector machines; BP neural network; BP-SVM; fuzzy clustering; intrusion detection system; neural network integration; support vector machine; Bagging; Boosting; Clustering algorithms; Fuzzy neural networks; Intrusion detection; Machine learning; Neural networks; Neurons; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Management and Service Science, 2009. MASS '09. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4638-4

Electronic_ISBN

978-1-4244-4639-1

Type

conf

DOI

10.1109/ICMSS.2009.5303886

Filename

5303886