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

HTTP Botnet Detection Algorithm Based on Content Association Recommendation

Authors
Jiajia Wang, Yu Chen
Corresponding Author
Jiajia Wang
Available Online March 2017.
DOI
10.2991/isaeece-17.2017.45How to use a DOI?
Keywords
HTTP protocol,botnet, recommendation algorithm
Abstract

HTTP botnet is widely distributed and causes great harm. The traditional detection method is not analyze the attack data stream until the attack stop. In order to further reduce the harm, according to the characteristics of http/https botnet, an online detection method based on HTTP protocol is proposed, which is based on the content association recommendation algorithm. This method is able to distinguish between normal data and malicious data, and complete detection before the attack start, without increasing the burden of the network because of small complexity. The experiment proves the feasibility of this method.

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
978-94-6252-334-0
ISSN
2352-5401
DOI
10.2991/isaeece-17.2017.45How 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  - HTTP Botnet Detection Algorithm Based on Content Association Recommendation
BT  - Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)
PB  - Atlantis Press
SP  - 240
EP  - 244
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-17.2017.45
DO  - 10.2991/isaeece-17.2017.45
ID  - Wang2017/03
ER  -