Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)

P2P Botnet Detection Method Based on Data Flow

Authors
Jiajia Wang, Yu Chen
Corresponding Author
Jiajia Wang
Available Online March 2017.
DOI
10.2991/isaeece-17.2017.44How to use a DOI?
Keywords
P2P, botnet, data stream, detection and prevention
Abstract

P2P data transmission is the mainstream of network data transmission. P2P botnet malicious data is hidden in normal transmit data, not only difficult to detect but also could cause great harm. This paper presents a method of P2P botnet detection based on data flow, first of all, extract the P2P data stream accurately, and then detect the P2P bots data stream, the small computational complexity does not affect the normal operation of the network. According to the characteristics of the P2P bots data stream , the detection method can detect the existence of the bots before the attack start and filter out illegal data. The experiment results show that this method has good detection efficiency and further maintains the security of the network.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
10.2991/isaeece-17.2017.44
ISSN
2352-5401
DOI
10.2991/isaeece-17.2017.44How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jiajia Wang
AU  - Yu Chen
PY  - 2017/03
DA  - 2017/03
TI  - P2P Botnet Detection Method Based on Data Flow
BT  - Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)
PB  - Atlantis Press
SP  - 235
EP  - 239
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-17.2017.44
DO  - 10.2991/isaeece-17.2017.44
ID  - Wang2017/03
ER  -