원문정보
보안공학연구지원센터(IJFGCN)
International Journal of Future Generation Communication and Networking
Vol.8 No.3
2015.06
pp.61-70
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보