Title :
A novel approach to intrusion detection base on fast incremental SVM
Author :
Qi Mu ; Yongjun Zhang ; Qian Niu
Author_Institution :
Sch. of Comput., Xi´an Univ. of Sci. & Technol., Xi´an, China
Abstract :
A new incremental SVM algorithm to intrusion detection based on cloud model is proposed for the low efficiency of border vectors extraction. In this algorithm, the characteristic distance between the heterogeneous samples is mapped into a membership function to extract the boundary vectors from initial dataset, which reflects the stability and uncertainty characteristics of the cloud model. Also the possible changes of support vector set after new samples adding are analyzed and the useless samples are discarded by the analysis results. The theoretical analysis and simulation results show that the detection speed is greatly improved, while maintaining a high detection performance.
Keywords :
cloud computing; security of data; support vector machines; border vectors extraction; boundary vectors extraction; characteristic distance; cloud model stability characteristics; cloud model uncertainty characteristics; detection performance; detection speed; fast incremental SVM algorithm; intrusion detection; membership function; support vector machines; Boundary Vectors; Cloud Model; Incremental Learning; Intrusion Detection; Support Vector Machine(SVM);
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4673-2963-7
DOI :
10.1109/ICCSNT.2012.6526197
