|Table of Contents|

[1] Song Yuanzhang, Chen Yuan, Wang Junjie, Wang Anbang, et al. Detection of P2P botnet based on network behavior featuresand Dezert-Smarandache theory [J]. Journal of Southeast University (English Edition), 2018, (2): 191-198. [doi:10.3969/j.issn.1003-7985.2018.02.008]
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Detection of P2P botnet based on network behavior featuresand Dezert-Smarandache theory()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
Issue:
2018 2
Page:
191-198
Research Field:
Computer Science and Engineering
Publishing date:
2018-06-20

Info

Title:
Detection of P2P botnet based on network behavior featuresand Dezert-Smarandache theory
Author(s):
Song Yuanzhang Chen Yuan Wang Junjie Wang Anbang Li Hongyu
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
Keywords:
P2P(peer-to-peer)botnet local singularity entropy Kalman filter Dezert-Smarandache theory
PACS:
TP393
DOI:
10.3969/j.issn.1003-7985.2018.02.008
Abstract:
In order to improve the accuracy of detecting the new P2P(peer-to-peer)botnet, a novel P2P botnet detection method based on the network behavior features and Dezert-Smarandache theory is proposed. It focuses on the network behavior features, which are the essential abnormal features of the P2P botnet and do not change with the network topology, the network protocol or the network attack type launched by the P2P botnet. First, the network behavior features are accurately described by the local singularity and the information entropy theory. Then, two detection results are acquired by using the Kalman filter to detect the anomalies of the above two features. Finally, the above two detection results are fused with the Dezert-Smarandache theory to obtain the final detection results. The experimental results demonstrate that the proposed method can effectively detect the new P2P botnet and that it considerably outperforms other methods at a lower degree of false negative rate and false positive rate, and the false negative rate and the false positive rate can reach 0.09 and 0.12, respectively.

References:

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Memo

Memo:
Biography: Song Yuanzhang(1986—), male, master, associate research fellow, songyuanzhang@163.com.
Foundation items: The National High Technology Research and Development Program of China(863 Program)(No.2011AA7031024G), the National Natural Science Foundation of China(No.61133011, 61373053, 61472161).
Citation: Song Yuanzhang, Chen Yuan, Wang Junjie, et al.Detection of P2P botnet based on network behavior features and Dezert-Smarandache theory[J].Journal of Southeast University(English Edition), 2018, 34(2):191-198.DOI:10.3969/j.issn.1003-7985.2018.02.008.
Last Update: 2018-06-20