Earticle

초록

영어

According to the relationship between feature subset and parameters of RBF neural network, in order to improve the intrusion detection accuracy, it proposed an improved particle swarm optimization neural network of network intrusion detection model. Network feature subset and parameters of RBF neural network were regarded as a particle, through collaboration and information exchange between particles to find the optimal feature subset and parameters of RBF neural network, so as to establish the optimal network intrusion detection model, and using KDD Cup 99 data sets to carry out simulation experiment. The simulation results showed that, IPSO-RBF neural network reduced the feature dimensions, and the better parameters of RBF neural network was obtained then, which is a kind of network intrusion detection model with high detection accuracy and high speed.

목차

Abstract
 1. Introduction
 2. Feature Selection and RBF Neural Network Parameter Optimization
  2.1. Feature Selection Optimization Problem
  2.2. RBF Neural Network Parameter Optimization Problem
  2.3. Joint Optimization of Feature Selection and RBF Neural Network Parameter
 3. Intrusion Detection Model of IPSO-RBF Neural Network
  3.1. Improve Particle Swarm Optimization Algorithm (The Expression of Formula(7) is Error)
  3.2. Network Intrusion detection procedure of IPSO-RBF Neural Network
 4. Simulation Experiment
  4.1. Data Sources
  4.2. Comparison Model and Evaluation Index
  4.3. Pre-Processing of the Data
  4.4. Results and Analysis
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Wang Xing-zhu [ Furong College Hunan University of Arts and Science Hunan Changde, 415000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605