Detecting DDoS attack based on PSO Clustering algorithm

DOI

10.2991/icmemtc-16.2016.133How to use a DOI?

Keywords

application-tier Distributed Denial of Service; browse behavior; particle clustering algorithm; anomaly detection.

Abstract

First, this article analyzes the Application layer Distributed Denial of Service(DDoS)'s attack principle and characteristic. According to the difference between normal users' browsing patterns and abnormal ones, user sessions are extracted from the web logs of normal users and similarities between different sessions are calculated .Because traditional K-mean Clustering algorithm is easy to fail into local optimal, the Particle Swarm Optimization K-mean Clustering algorithm is used to generate a detecting model. This model can been usedto detect whether the undetermined sessions are DDoS attacks or not. The experiment show that this method can detect attacks effectively and have a good performance in adaptability.

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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TY  - CONF
AU  - Xiaohong Hao
AU  - Boyu Meng
AU  - Kaicheng Gu
PY  - 2016/04
DA  - 2016/04
TI  - Detecting DDoS attack based on PSO Clustering algorithm
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 670
EP  - 674
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.133
DO  - 10.2991/icmemtc-16.2016.133
ID  - Hao2016/04
ER  -