Earticle

초록

영어

In recent years, the problem of network security has been more and more people's attention, as one of the most important technology of network security, intrusion detection technology has gone through nearly thirty years of development, but it still exists some deficiency factors. Aiming at the defects of the traditional BP neural network intrusion detection model in the detection rate and the convergence speed, the improved PSO-BP neural network is applied to intrusion detection system model in this paper. Experimental and simulation, verifying the improved effect of system in the false negative rate, false positives rate and convergence speed of. Detailed analysis of the standard BP neural network algorithm and improved way of common, including gradient descent algorithm and additional momentum algorithm. Local search capability of BP neural network and the global search ability of particle swarm optimization , we have a detailed description of the PSO algorithm is applied to the case of BP neural network and discusses the improved PSO-BP neural network algorithm flow.

목차

Abstract
 1. Introduction
 2. Related Research
 3. Proposed Scheme
  3.1 Research of BP Neural Network Algorithm
  3.2. Related Research
 4. Experimental Results and Analysis
  4.1 IDS Performance Indicators
  4.2 Performance Testing
 5. Conclusion
 6. Project
 References

키워드

저자

• Chunmin Qiu [ Binzhou Polytechnic, Binzhou Shandong,256603,China ] First Author
• Jie Shan [ Binzhou Polytechnic, Binzhou Shandong,256603,China ] Corresponding Author

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