Earticle

초록

영어

Botnet is most widespread and occurs commonly in today's cyber attacks and they become one of the most serious threats on the Internet. Most of the existing Botnet detection approaches concentrate only on particular Botnet command and control (C&C) protocols and structures, and can become ineffective as Botnets change their structure and C&C techniques. In this paper, we proposed a new general detection strategy. This proposed strategy was based on degree distributions of node and anomaly net flows, and combined data mining technology. In this scheme, we first constructed accurate traffic profile based on packet behavioral mode, and then introduced dialog flow to draw traffic profile of node. Finally we set up degree distributions of node and group and applied the degree distributions of node as input of data mining, which were then classified and distinguished to obtain reliable results with acceptable accuracy. The advantages of our proposed detection method is that there is no need for prior knowledge of Botnets such as Botnet signature and the accuracy of the experiment results is as much as 99%. The FP rate and the FN rate can be controlled within 3%, the best is almost 0.

목차

Abstract
 1. Introduction
 2. Botnet Abnormal Behavior Analysis
 3. Attribute Analysis
 4. The Architecture of Our Detection Framework
 5. Result and Discussion
 6. Conclusions and Future Work
 Acknowledgements
 References

키워드

저자

• Chunyong Yin [ School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China, Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Lei Yang [ School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Jin Wang [ School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, 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