DocumentCode
596567
Title
Intrusion detection system based on improved BP Neural Network and Decision Tree
Author
Jinhua Huang ; Jiqing Liu
Author_Institution
Dept. of Electr. & Electron. Eng., Wuhan Inst. of Shipbuilding Technol., Wuhan, China
fYear
2012
fDate
18-20 Oct. 2012
Firstpage
188
Lastpage
190
Abstract
According to the attributes of both BP Neural Network and Decision Tree, this paper presents an advanced complex-algorithm model in order to improve the ability of intrusion detection. The simulating results show that the new system not only can increase the average detection rate and reduce the failing, but it can also be more effective to simplify the complexity, raise the detection speed and promote the accuracy by use of abstracting rules and paralleled dealing method in matching process.
Keywords
backpropagation; decision trees; neural nets; security of data; BP neural network; abstracting rule; backpropagation neural network; decision tree; detection accuracy; detection rate; detection speed; intrusion detection system; matching process; paralleled dealing method; Algorithm design and analysis; Decision trees; Intrusion detection; Neural networks; Testing; Training; Valves;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4673-1743-6
Type
conf
DOI
10.1109/ICACI.2012.6463148
Filename
6463148
Link To Document