DDoS Detection and Prevention Based on Joint Entropy and Conditional Entropy

Yong Hao Gu; Wei Ming Wu

doi:10.4028/www.scientific.net/KEM.474-476.2129

Paper Titles

Preparation and Properties of Polyethylene/Montmorillonite Composites
p.2110

Fatigue Life Prediction Based on GA-BP Algorithm
p.2114

An Improved Crawler Algorithm Based on Hierarchical Structure Preservation
p.2120

Application of DCP to Evaluate Subgrade Compaction Quality
p.2125

DDoS Detection and Prevention Based on Joint Entropy and Conditional Entropy
p.2129

Effect of Nanocrystalline and Ti Implantation on the Oxidation Behaviour of Fe₈₀Cr₂₀Alloy and Commercial Ferritic Steel
p.2134

The Study of Forest Fire Color Image Segmentation
p.2140

The 3-D Simulation of the LNGC in Pre-Cooling Process
p.2146

Based on RFID Food Supply Chain Traceability System Framework Design
p.2150

HomeKey Engineering MaterialsKey Engineering Materials Vols. 474-476DDoS Detection and Prevention Based on Joint...

DDoS Detection and Prevention Based on Joint Entropy and Conditional Entropy

Abstract:

Distributed Denial of Service (DDoS) imposes a very serious threat to the stability of the Internet. Compared with many detection approaches, detecting DDoS attacks based on entropy has advantages such as simplicity, high sensitivity and low false positive rate. But the method with single attribute entropy has high false positive rate when detecting attribute forged attacks. This paper presents a detecting method based on joint entropy and a filtering way based on conditional entropy. The efficiency of this scheme is validated with simulation on the research lab network.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Key Engineering Materials (Volumes 474-476)

Pages:

2129-2133

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.474-476.2129

Citation:

Cite this paper

Online since:

April 2011

Authors:

Yong Hao Gu, Wei Ming Wu

Keywords:

Conditional Entropy, Distribute Denial of Services (DDoS), Joint Entropy

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Lawrence A Gordon, Martin P Loeb, William Lucyshyn, et al. 2004 CSI/FBI computer crime and security survey [R]. Technical Report, Computer Security Institute, (2004).

Google Scholar

[2] ZHANG Deng-yin, LIU Yu, LUO Tao, DDoS traceback based on router traffic analysis [J]. Journal of Jiangsu University: Natural Science Edition, 2007, 28(6): 516-519.

Google Scholar

[3] YU SHUI, ZHOU WANLEI. Entropy-based collaborative detection of DDoS attacks on community networks [C] / Proceedings of the 2008 6th Annual IEEE International Conference on Pervasive Computing and Communications. Washington, DC: IEEE Computer Society, 2008: 566 - 571.

DOI: 10.1109/percom.2008.12

Google Scholar

[4] YAN Ruoyu, ZHENG Qinghua. Using Cross Entropy to Detect and Classify Network Anomalous Traffic[J]. Journal of Xi'an Jiaotong University, 2010, 44(6): 10-15.

Google Scholar

[5] ZHANG Jie, QIN Zheng. Modified method of detecting DDoS attacks based on entropy[J]. Journal of Computer Applications, 2010, 30(7): 1778-1781.

DOI: 10.3724/sp.j.1087.2010.01778

Google Scholar

[6] L. Feinstein, D. Schnackenberg, R. Balupari, and D. Kindred. Statistical Approaches to DDoS Attack Detection and Response[C]. /DARPA Information Survivability Conference and Exposition (DISCEX), 2003: 303-314.

DOI: 10.1109/discex.2003.1194894

Google Scholar

[7] Rahmani, H., Sahli, N., Kammoun, F. Joint Entropy Analysis Model for DDoS Attack Detection[C]. / 2009 Fifth International Conference on Information Assurance and Security, 2009: 267-271.

DOI: 10.1109/ias.2009.298

Google Scholar

[8] S. Templeton and K. Levitt, Detecting spoofed packets[C]. /Proceedings of 3rd DARPA Information Survivability Conf. and Expo. (DISCEX III), 2003: 164–175.

DOI: 10.1109/discex.2003.1194882

Google Scholar