Title :
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
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;
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
DOI :
10.1109/ICMSS.2009.5303886
