Title :
Network security risk assessment based on support vector machine
Author :
Chen, Jun ; Tu, Xionggang
Author_Institution :
Sch. of Comput. Sci., Zhejiang Ind. Polytech. Coll., Shaoxing, China
Abstract :
With the development and application of network technology, the issues of network security has become prominent increasingly. Network security risk assessment has become the key process in solve network security. Support Vector Machine(SVM)is one of novel learning machine methods, its advantages are simple structure, strong compatibility, global optimization, least raining time and better generalization. So it has superiority to apply it into network security risk assessment. This paper describes the content and the evaluation indicators of network security risk assessment and the classification of the support vector machine in detail. And then an assessment method of network security risk based on support vector machine is proposed in this paper. Experiment results show that the method Is feasible and effective.
Keywords :
learning systems; support vector machines; telecommunication security; learning machine methods; network security risk assessment; support vector machine; Accuracy; Educational institutions; Kernel; Risk management; Security; Support vector machines; Training; Network Security; Risk Assessment; Support Vector Machine(SVM);
Conference_Titel :
Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-61284-485-5
DOI :
10.1109/ICCSN.2011.6013690