Decision Analysis of Statistically Detecting Distributed Denial-of-Service Flooding Attacks-Reference-Cited by-同舟云学术

Decision Analysis of Statistically Detecting Distributed Denial-of-Service Flooding Attacks

Published:2003-09 Issue:03 Volume:02 Page:397-405
ISSN:0219-6220
Container-title:International Journal of Information Technology & Decision Making
language:en
Short-container-title:Int. J. Info. Tech. Dec. Mak.

Author:

Li Ming¹²,Chi Chi-Hung¹,Jia Weijia³,Zhao Wei⁴,Zhou Wanlei⁵,Cao Jiannong⁶,Long Dongyang⁷,Meng Qiang⁸

Affiliation:

1. School of Computing, National University of Singapore, Singapore 117540, Singapore

2. School of Information Technology, Southern Yangtze University, Wuxi 214036, PR China

3. Department of Computer Engineering and Information Technology, City University of Hong Kong, Hong Kong, SAR China

4. Department of Computer Science, Texas A&M University, College Station, USA

5. School of Information Technology, Deakin University, Australia

6. Department of Computer, Hong Kong Polytechnic University, Hong Kong, SAR China

7. Department of Computer Science, Zhongshan University, Guangzhou 510275, PR China

8. Department of Civil Engineering, National University of Singapore, Singapore 117576, Singapore

Abstract

There are two statistical decision making questions regarding statistically detecting sings of denial-of-service flooding attacks. One is how to represent the distributions of detection probability, false alarm probability and miss probability. The other is how to quantitatively express a decision region within which one may make a decision that has high detection probability, low false alarm probability and low miss probability. This paper gives the answers to the above questions. In addition, a case study is demonstrated.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Science (miscellaneous),Computer Science (miscellaneous)

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0219622003000720

Reference5 articles.

1. Denial-of-service attacks rip the internet

2. Wide area traffic: the failure of Poisson modeling

3. Digital Pattern Recognition

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