Towards lightweight and efficient DDOS attacks detection for web server

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Towards lightweight and efficient DDOS attacks detection for web server

Full text

Pdf (669 KB)

Source

International World Wide Web Conference archive
Proceedings of the 18th international conference on World wide web table of contents

Madrid, Spain

POSTER SESSION: Thursday, April 23, 2009 table of contents

Pages 1139-1140

Year of Publication: 2009

ISBN:978-1-60558-487-4

Authors

Yang Li	China Mobile Research Institute, Beijing, China
Tian-Bo Lu	National Computer network Emergency Response technical Team/Coordination Center of China, Beijing, China
Li Guo	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Zhi-Hong Tian	Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Qin-Wu Nie	Hunan University of Science and Technology, Xiangtan, China

Sponsor

ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 21, Downloads (12 Months): 124, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1526709.1526896 What is a DOI?

ABSTRACT

In this poster, based on our previous work in building a lightweight DDoS (Distributed Denial-of-Services) attacks detection mechanism for web server using TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) and genetic algorithm based instance selection methods, we further propose a more efficient and effective instance selection method, named E-FCM (Extend Fuzzy C-Means). By using this method, we can obtain much cheaper training time for TCM-KNN while ensuring high detection performance. Therefore, the optimized mechanism is more suitable for lightweight DDoS attacks detection in real network environment.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Glenn Carl , George Kesidis , Richard R. Brooks , Suresh Rai, Denial-of-Service Attack-Detection Techniques, IEEE Internet Computing, v.10 n.1, p.82-89, January 2006 [doi>10.1109/MIC.2006.5]
	2	Erol Gelenbe , George Loukas, A self-aware approach to denial of service defence, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.51 n.5, p.1299-1314, April, 2007 [doi>10.1016/j.comnet.2006.09.009]
	3	Jelena Mirkovic , Peter Reiher , Sonia Fahmy , Roshan Thomas , Alefiya Hussain , Stephen Schwab , Calvin Ko, Measuring denial Of service, Proceedings of the 2nd ACM workshop on Quality of protection, October 30-30, 2006, Alexandria, Virginia, USA [doi>10.1145/1179494.1179506]
	4	Y. Li, L. Guo, B. X. Fang, Z. H. Tian, Y. Z. Zhang. "Detecting DDoS Attacks Against Web Server via Lightweight TCM-KNN Algorithm". In Proc. Of ACM Sigcomm 2008 (SIGCOMM 2008), pp: 497--498.
	5	Yang Li , Li Guo , Zhi-Hong Tian , Tian-Bo Lu, A lightweight web server anomaly detection method based on transductive scheme and genetic algorithms, Computer Communications, v.31 n.17, p.4018-4025, November, 2008 [doi>10.1016/j.comcom.2008.08.009]