Earticle

초록

영어

With the rapid development of the Internet and the continuous expanding of the data network, little potential anomaly can seriously affect the normal operation of the network, and even lead to huge economic losses. In order to be more accurate and efficient in the traffic detection, in this paper, we propose an N-ARMA based traffic anomaly detection model. We also conduct extensive experiments to verify the higher accurate ratio and recall ratio of our model by comparing with other traffic anomaly detection methods.

목차

Abstract
 1. Introduction
 2. Related Work
 3. ARMA Model
 4. N-ARMA Traffic Detection Model
  4.1. N-ARMA Model Introduction
  4.2. N-ARMA Model Building
 5. Experiments and Analysis
  5.1. Data Preprocessing
  5.2. N-ARMA Model Building
  5.3. Outlier Detection Experiments
 6. Conclusions
 References

키워드

저자

• Pingping Gu [ Computer Science Department, Tan Kah Kee College Xiamen University, China ]
• Shijing Zhang [ Software School, Xiamen University, Xiamen, China ]
• Zhimin Huang [ Software School, Xiamen University, Xiamen, China ]
• Qingfeng Wu [ Corresponding Author Software School, Xiamen University, China ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605