Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

Detection of Application-Layer DDoS by Clustering Algorithm

Authors
Chuyu She, Wushao Wen, Zaihua Lin, Kesong Zheng
Corresponding Author
Chuyu She
Available Online November 2016.
DOI
10.2991/aiie-16.2016.25How to use a DOI?
Keywords
application-layer; DDoS attack; affinity propagation; clustering algorithm; features
Abstract

Affinity Propagation (AP) algorithm is a relatively new clustering algorithm that can handle large datasets to obtain more satisfactory results. This paper introduces a detection mechanism for application-layer DDoS attack by using AP algorithm. In this detection strategy, we first extract some features from normal users' sessions. Then, we cluster these normal users' sessions by AP algorithm to get K clusters. Finally, we use these models to detect application-layer DDoS attacks.

Copyright
© 2016, 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 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-271-8
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.25How to use a DOI?
Copyright
© 2016, 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  - Chuyu She
AU  - Wushao Wen
AU  - Zaihua Lin
AU  - Kesong Zheng
PY  - 2016/11
DA  - 2016/11
TI  - Detection of Application-Layer DDoS by Clustering Algorithm
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 102
EP  - 104
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.25
DO  - 10.2991/aiie-16.2016.25
ID  - She2016/11
ER  -