Title :
Optimization Algorithm Based on Genetic Support Vector Machine Model
Author :
Lan Li ; Shaobin Ma ; Yun Zhang
Author_Institution :
Dept. of Comput., Lanzhou Univ. of Arts & Sci., Lanzhou, China
Abstract :
Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use the model to detect. The experiments choose the proper parameters (c, s) through discussing the influence of support vector machines parameters to the detection accuracy. The results show that putting genetic support vector machine model into intrusion detection improved detection accuracy.
Keywords :
genetic algorithms; security of data; support vector machines; genetic algorithm; genetic support vector machine model; intrusion detection system; optimization algorithm; Accuracy; Classification algorithms; Genetic algorithms; Genetics; Intrusion detection; Kernel; Support vector machines; genetic algorithms; genetic support vector machine model; intrusion detection; support vector machine;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN :
978-1-4799-7004-9
DOI :
10.1109/ISCID.2014.99