DocumentCode :
3178529
Title :
Research of Information Security Risk Management Based on Statistical Learning Theory
Author :
Li, Zhao ; Yongchun, Wu ; Xuexia, Wu
Author_Institution :
Sch. of Bus., Shandong Jianzhu Univ., Jinan, China
Volume :
3
fYear :
2009
fDate :
25-27 Dec. 2009
Firstpage :
436
Lastpage :
438
Abstract :
Traditional methods used in the information security risk management are mostly based on the statistics, their validity of application are limited to large sample situations, while the statistical learning theory is introduced to the innovation of information security management, its structural risk minimization principle and from which the support vector machine developed offer new theory basis for predicting the security risk and achieve the minimal risk. This paper not only detailedly discusses the structural risk minimization principle and from which the support vector machine developed offer new theory basis for predicting the security risk and achieve the minimal risk, but also puts forwards the idea of using structural risk minimization principle and the support vector machine in information security risk management, which provides the brand-new mentality to the information security risk management.
Keywords :
learning (artificial intelligence); risk management; security of data; statistics; support vector machines; information security risk management; statistical learning theory; structural risk minimization principle; support vector machine; EMP radiation effects; H infinity control; Information management; Information security; Resource management; Risk management; Statistical learning; Statistics; Support vector machines; Technology management; information security risk management; statistical learning theory; structural risk minimization; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on
Conference_Location :
Chongqing
Print_ISBN :
978-0-7695-3930-0
Electronic_ISBN :
978-1-4244-5423-5
Type :
conf
DOI :
10.1109/IFCSTA.2009.346
Filename :
5384916
Link To Document :
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