Anomaly Detection for DDoS Attacks Based on Gini Coefficient

DOI

10.2991/icaicte.2013.129How to use a DOI?

Keywords

anomaly detection, Gini coefficient, TCM-KNN algorithm

Abstract

Distributed Denial-of-Service (DDoS) attacks present a very serious threat to the stability of the Internet. In this paper, an anomaly detection method for DDoS attacks based on Gini coefficient is pro-posed. First, Gini coefficient is introduced to measure the inequalities of packet attribution (IP addresses and ports) distributions during attacks. Then, an im-proved TCM-KNN algorithm is applied to identify attacks by classifying the Gini coefficient samples extracted from real-time network traffic. The experimental results demonstrate that the proposed method can effectively distinguish DDoS attacks from normal traffic, and has higher detection ratio and lower false alarm ratio than similar detection methods.

Copyright

© 2013, 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  - Yun Liu
AU  - Siyu Jiang
AU  - Jiuming Huang
PY  - 2013/08
DA  - 2013/08
TI  - Anomaly Detection for DDoS Attacks Based on Gini Coefficient
BT  - Proceedings of the 2013 International Conference on Advanced ICT and Education
PB  - Atlantis Press
SP  - 632
EP  - 637
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaicte.2013.129
DO  - 10.2991/icaicte.2013.129
ID  - Liu2013/08
ER  -