DocumentCode :
2600007
Title :
Research on Network Security Situation Prediction-Oriented Adaptive Learning Neuron
Author :
Li, Jing ; Dong, Chunbo
Author_Institution :
Sch. of Comput. Sci. & Inf. Eng., Harbin Normal Univ., Harbin, China
Volume :
2
fYear :
2010
fDate :
24-25 April 2010
Firstpage :
483
Lastpage :
485
Abstract :
Network security situation perception is to predict the probability of attacks, may occur in the future, by a variety of predicting methods, by recent network attacking data obtained from IDS (Intrusion Detection System). Neural Network model has many features, high degree of fault tolerance, associability, self-organizing and self-learning ability, and strong nonlinear mapping and generalization for a complex system, for example. Therefore, Neural Network was applied to the field of network security situation prediction. Adaptive Learning of neuron was introduced. It will be more flexibility to meet changing security environment of such a complex system requirements. The design and achievement of the adaptive learning neuron was stated in detail.
Keywords :
large-scale systems; learning (artificial intelligence); neural nets; security of data; complex system; fault tolerance; generalization; intrusion detection system; network security situation; neural network; nonlinear mapping; prediction oriented adaptive learning neuron; self-learning ability; self-organizing ability; Artificial neural networks; Biological neural networks; Computer security; Data security; Fault tolerant systems; Humans; Information security; Intelligent networks; Intrusion detection; Neurons; adaptive learning neuron; network security; neural network; situation prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Networks Security Wireless Communications and Trusted Computing (NSWCTC), 2010 Second International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-4011-5
Electronic_ISBN :
978-1-4244-6598-9
Type :
conf
DOI :
10.1109/NSWCTC.2010.247
Filename :
5480921
Link To Document :
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