Earticle

초록

영어

To recognize abnormal traffic in network, so as to perceive the illicit behavior in network, carry out scientific and effective management, and ensure the network security, we extracted the abnormal network traffic features and proposed an abnormal network traffic recognition method based on optimized Back Propagation Artificial Neural Networks (BP ANN). The experimental results indicate that, although the training time is longer, but the accuracy rate of BP ANN in abnormal network traffic identification is superior to other methods. And the convergence rate of optimized BP ANN model is significantly faster than traditional BP ANN model.

목차

Abstract
 1. Introduction
 2. The Survey of Recognition Methods of Network Traffic
 3. The Basic Principle of BP ANN
 4. The optimization of BP ANN model and corresponding algorithm
  4.1 The design and optimization of network structure
  4.2 The optimization of learning factor η
 5. Experiment Process and Experiment Results
  5.1 The Data Collecting
  5.2 The Experiment Procedure
  5.3 The Experiment Results
 6. Conclusion
 References

키워드

저자

• Xu Yabin [ School of Computer, Beijing Information Science and Technology University, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605