Earticle

초록

영어

To detect various network attacks in real time, this paper developed a network intrusion detection system based on artificial neural network. This paper first introduced the recent development of neural network, BP algorithm and structure of a simple perceptron. Then, this paper developed an improved BP neural network algorithm to detect anomaly network traffic with adjusted correlation rules. Finally, the network intrusion system in this paper was tested in a real network situation; the improved BP algorithm neural network with adjusted correlation rules shows a reduction in total error and increment in alarm rate compared to the traditional basic BP algorithm model.

목차

Abstract
 1. Introduction
 2. Methods
  2.1. Improvement of the Basic BP Algorithm
  2.2. Correlation Analysis
  2.3. The Process of Network Intrusion Detection
 3. Results
 4.Conclusion
 Acknowledgments
 References

키워드

저자

• Yongfeng Cui [ School of Science and Technology, Zhoukou Normal University, Zhoukou Henan 466001, China ] Corresponding author
• Xiangqian Li Ma [ Network Centre, Zhoukou vocational and technical college, Zhoukou Henan 466000, China ]
• Zhijie Liu [ Library, Zhoukou Normal University, Zhoukou Henan 466001, 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