Title :
A Network Security Dynamic Situation Forecasting Method
Author :
Juan, Li ; Tao, Li ; Gang, Liang
Author_Institution :
Sch. of Comput. Sci., Sichuan Univ., Chengdu, China
Abstract :
This paper proposes a network security dynamic situation forecasting method (Unbiased Gray Markov Forecasting Method: UGM_HM), which based on the Unbiased Grey system theory and Markov Forecasting theory. UGM_HM combines advantages of Unbiased Grey system theory and Markov Forecasting theory. UGM_HM takes the complex network environment as a Grey system and takes the dynamic risk value of network as a Grey value. The long-term network security situation is reflected by the Unbiased GM (1, 1) and the state transition probabilities are identified by Markov chain theory. The above mentioned dynamic risk value of network, which based on the artificial immune can reflect the network real-time state. The conclusions of experiment prove that UGM_HM compares with Unbiased GM (1, 1) has high precision in the forecasting of the network security dynamic situation.
Keywords :
Markov processes; artificial immune systems; forecasting theory; grey systems; least squares approximations; Markov chain theory; artificial immune system; complex network environment; dynamic situation forecasting method; network security dynamic situation; state transition probabilities; unbiased Gray Markov forecasting method; unbiased Grey system theory; Application software; Complex networks; Computer security; Detectors; Event detection; Immune system; Information security; Information technology; Intrusion detection; Technology forecasting; Artificial Immune; Grey Markov Forecasting Theory; Prediction of Network Situation;
Conference_Titel :
Information Technology and Applications, 2009. IFITA '09. International Forum on
Print_ISBN :
978-0-7695-3600-2
DOI :
10.1109/IFITA.2009.42