DocumentCode
3312603
Title
Network Intrusion Detection Method Based on High Speed and Precise Genetic Algorithm Neural Network
Author
Tian, Jingwen ; Gao, Meijuan
Author_Institution
Dept. of Autom. Control, Beijing Union Univ., Beijing
Volume
2
fYear
2009
fDate
25-26 April 2009
Firstpage
619
Lastpage
622
Abstract
Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of neural network, an intrusion detection method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the network intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.
Keywords
backpropagation; genetic algorithms; neural nets; security of data; BP network; floating-point code genetic algorithm; intrusion behaviors; network intrusion detection method; neural network; Artificial intelligence; Artificial neural networks; Computer networks; Convergence; Genetic algorithms; Information security; Intrusion detection; Neural networks; Uncertainty; Wireless communication; Network; genetic algorithm; intrusion detection; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Networks Security, Wireless Communications and Trusted Computing, 2009. NSWCTC '09. International Conference on
Conference_Location
Wuhan, Hubei
Print_ISBN
978-1-4244-4223-2
Type
conf
DOI
10.1109/NSWCTC.2009.228
Filename
4908545
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