An improved wavelet analysis method for detecting DDoS attacks

Liang Fu Lu, Mao Lin Huang, Mehmet A. Orgun, Jia Wan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)
10 Downloads (Pure)


Wavelet Analysis method is considered as one of the most efficient methods for detecting DDoS attacks. However, during the peak data communication hours with a large amount of data transactions, this method is required to collect too many samples that will greatly increase the computational complexity. Therefore, the real-time response time as well as the accuracy of attack detection becomes very low. To address the above problem, we propose a new DDoS detection method called Modified Wavelet Analysis method which is based on the existing Isomap algorithm and wavelet analysis. In the paper, we present our new model and algorithm for detecting DDoS attacks and demonstrate the reasons of why we enlarge the Hurst's value of the self-similarity in our new approach. Finally we present an experimental evaluation to demonstrate that the proposed method is more efficient than the other traditional methods based on wavelet analysis.

Original languageEnglish
Title of host publicationProceedings - 2010 4th International Conference on Network and System Security, NSS 2010
EditorsYang Xiang, Pierangela Samarati, Jiankun Hu, Wanlei Zhou, Ahmad-Reza Sadeghi
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)9780769541594
Publication statusPublished - 2010
Event4th International Conference on Network and System Security, NSS 2010 - Melbourne, VIC, Australia
Duration: 1 Sep 20103 Sep 2010


Other4th International Conference on Network and System Security, NSS 2010
CityMelbourne, VIC

Bibliographical note

Copyright 2010 IEEE. Reprinted from 2010 Fourth International Conference on Network and System Security : proceedings : NSS 2009 : 1-3 September 2010, Melbourne, Australia. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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