Earticle

초록

영어

Security problems with network are significant, such as network failures and malicious attacks. Monitoring network traffic and detect anomalies of network traffic is one of the effective manner to ensure network security. In this paper, we propose a hybrid method for network traffic prediction and anomaly detection. Specifically, the original network traffic data is decomposed into high-frequency components and low-frequency components. Then, non-linear model Relevance Vector Machine (RVM) model and ARMA (Auto Regressive Moving Average) model are employed respectively for prediction. After combining the prediction, a self-adaptive threshold method based on Central Limit Theorem (LCT) is introduced for anomaly detection. Moreover, our extensive experiments evaluate the efficiency of proposed method.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Network Traffic Prediction Model
  3.1. Wavelet Decomposition
  3.2. RVM Based Prediction
 4. Anomaly Detection of Network Traffic
 5. Experiment
  5.1. Dataset Description
  5.2. Self-Similarity Analysis
  5.3. Prediction Results
 6. Conclusion
 References

키워드

저자

• Haiyan Wang [ Department of Information Engineering, Binzhou University, Binzhou, Shandong, China, 256600 ]

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