scholarly journals A new Immunity Intrusion Detection Model Based on Genetic Algorithm and Vaccine Mechanism

2010 ◽  
Vol 2 (2) ◽  
pp. 33-39 ◽  
Author(s):  
Xiao-Pei Jing ◽  
Hou-Xiang Wang
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Detection Model ◽  
Model Based

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%.

Network Intrusion Detection Model Based on Genetic Algorithm Optimizing Parameters of Support Vector Machine

2014 ◽  
Vol 989-994 ◽  
pp. 2012-2015
Author(s):  
Chun Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Intrusion Detection ◽  
Computer Security ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Detection Model ◽  
Model Based ◽  
Network Intrusion ◽  
Parameters Selection

Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Parameters selection of support vector machine is a important problems in network intrusion detection. In order to improve network intrusion detection precision, this paper proposed a network intrusion detection model based on parameters of support vector machine (SVM) by genetic algorithm. The performance of the model was tested by KDD Cup 99 data. Compared with other network intrusion detection models, the proposed model has significantly improved the detection precision of network intrusion.

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