Study on Intrusion Detection Model Based on Improved Genetic Algorithm and Fuzzy Neural Network

2012 ◽  
pp. 1163-1169
Author(s):  
Jinguang Chen ◽  
Yuesheng Gu ◽  
Zhixiong Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Fuzzy Neural Network ◽  
Improved Genetic Algorithm ◽  
Detection Model ◽  
Model Based ◽  
Fuzzy Neural

Intrusion Detection Model Based on Improved Genetic Algorithm Neural Network in Computer Integrated Process System

2013 ◽  
Vol 380-384 ◽  
pp. 2708-2711
Author(s):  
Li Kun Zou ◽  
Shao Kun Liu ◽  
Guo Fu Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Detection System ◽  
Linear Mapping ◽  
Integrated Process ◽  
Detection Model ◽  
Model Based ◽  
Fitness Value ◽  
Process System

In order to solve the problems of high false alarm rate and fail rate in intrusion detection system of Computer Integrated Process System (CIPS) network, this paper takes advantage that Genetic Algorithm (GA) possesses overall optimization seeking ability and neural network has formidable approaching ability to the non-linear mapping to propose an intrusion detection model based on Genetic Algorithm Neural Network (GANN) with self-learning and adaptive capacity, which includes data collection module, data preprocessing module, neural network analysis module and intrusion alarm module. To overcome the shortcomings that GA is easy to fall into the extreme value and searches slowly, it improves the adjusting method of GANN fitness value and optimizes the parameter settings of GA. The improved GA is used to optimize BP neural network. Simulation results show that the model makes the detection rate of the system enhance to 97.11%.

Fuzzy Neural Network Controller Based on Improved Genetic Algorithm for Temperature of Glass Tempering and Annealing Process

2010 ◽  
Vol 154-155 ◽  
pp. 214-219
Author(s):  
Xiao Kan Wang ◽  
Zhong Liang Sun ◽  
Sanci Guo ◽  
Chao Qun Shen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Annealing Process ◽  
Time Varying ◽  
Improved Genetic Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Time Varying Parameters ◽  
Fuzzy Neural

The temperature control of the glass tempering and annealing process has characteristics of time-varying parameters，nonlinear and big lag. It is difficult to meet the expected control effect with the common control method. To solve this problem，this paper puts forward a kind of fuzzy neural network controller optimized by genetic algorithm. First，it uses neural network to construct fuzzy logic system according to the structure equivalence rule，thus the optimization of fuzzy control rules and membership functions can be realized by finding the weight value of the neural network. Then，it uses the improved genetic algorithm to find the global optimum weighted factors with a high speed so to improve the performance of the controller. The simulation results show that the optimized fuzzy neural network controller can obtain an excellent control performance for the nonlinearity system with time- varying parameters and lag.

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