Improved Genetic Algorithm in Intrusion Detection Model Based on Artificial Immune Theory

2009 ◽  
Author(s):  
Xiaopei Jing ◽  
Houxiang Wang ◽  
Ruofei Han ◽  
Juan Li
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Improved Genetic Algorithm ◽  
Artificial Immune ◽  
Detection Model ◽  
Model Based

Integrated intrusion detection model based on artificial immune

2014 ◽  
Vol 21 (2) ◽  
pp. 83-90 ◽  
Author(s):  
Ling ZHANG ◽  
Zhong-ying BAI ◽  
Yun-long LU ◽  
Ya-xing ZHA ◽  
Zhen-wen LI
Keyword(s):  
Intrusion Detection ◽  
Artificial Immune ◽  
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%.

A Network Intrusion Detection Model Based on Artificial Immune

2011 ◽  
Vol 361-363 ◽  
pp. 687-690 ◽  
Author(s):  
Xin Xiao ◽  
Rui Rui Zhang
Keyword(s):  
Intrusion Detection ◽  
Clustering Algorithm ◽  
False Positive Rate ◽  
Artificial Immune Systems ◽  
Affinity Maturation ◽  
Network Intrusion Detection ◽  
Artificial Immune ◽  
Detection Model ◽  
Model Based ◽  
Network Intrusion

For the existing artificial immune systems applied to network intrusion detection have some shortages, an improved network intrusion detection model based on the dynamic clone selection algorithm which was put forward by Kim is proposed. The model introduces the concept of self group, which is obtained by the clustering algorithm AiNet and represents common features of normal data. The self group deals with network data before they are tested by detectors. In addition, the model adopts a design of distributed network intrusion detection, and a central server manages all the immune cells, receives vaccines and vaccinats the whole network detection hosts. Experimental results show that the number of selves and detectors are reduced, the process of affinity maturation for the detector population is speeded up, and the model achieves higher detection rate and lower false positive rate with the evolution generation increases.

scholarly journals Network Intrusion Detection Based on Sparse Autoencoder and IGA-BP Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongli Deng ◽  
Tao Yang
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Network Model ◽  
Detection Rate ◽  
Network Intrusion Detection ◽  
Improved Genetic Algorithm ◽  
Bp Network ◽  
Model Based ◽  
Network Intrusion ◽  
Different Types

Network intrusion detection system provides a better network security solution than other traditional network defense technologies. Aiming at the increasingly serious problem of Internet security in the big data environment, a network intrusion detection model based on autoencoder network model and improved genetic algorithm BP (IGA-BP) network is constructed. In order to reduce the data dimension and eliminate redundant information, the autoencoder network model is firstly used to denoise and dedimension. A new population was formed by selecting some of the best parent individuals for cross mutation and replacing the worst parent individuals. The improved genetic algorithm and new population generation model will provide more reasonable initial parameters for BP network, namely, IGA-BP network model. Based on IGA-BP network model, the problems of slow detection rate and easy to get into local optimality in BP network are solved. The experiments were performed on KDD CUP99 dataset, which simulated different types of user organizations and different types of network intrusion. Compared with the existing intrusion detection methods, the experimental results show that the proposed method has a great effect on classification accuracy, false positives, and detection rate.

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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