Title :
Intrusion Detection Based on Support Vector Machine Using Heuristic Genetic Algorithm
Author :
Tao Yerong ; Sui Sai ; Xie Ke ; Liu Zhe
Author_Institution :
China Luoyang Electron. Equip. Test Center, Luoyang, China
Abstract :
The parameters of Support Vector Machine (SVM) are optimized using heuristic genetic algorithm and then to detect the network intrusion behavior. The heuristic real-coded genetic algorithm is used to optimize the best parameters of SVM with Gauss kernel aimed at the classification accuracy of the model. The classification accuracy is largely improved. Experimental results show that this method has a broad application future.
Keywords :
computer network security; genetic algorithms; pattern classification; support vector machines; Gauss kernel; SVM; classification accuracy; heuristic genetic algorithm; intrusion detection; network intrusion behavior; support vector machine; Accuracy; Classification algorithms; Genetic algorithms; Intrusion detection; Optimization; Support vector machines; Training; genetic algorithm; intrusion detection; support vector machines;
Conference_Titel :
Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on
Conference_Location :
Bhopal
Print_ISBN :
978-1-4799-3069-2
DOI :
10.1109/CSNT.2014.143
