An improved wavelet analysis method for detecting DDoS attacks

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

doi:10.1109/NSS.2010.23

An improved wavelet analysis method for detecting DDoS attacks

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

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

9 Citations (Scopus)

23 Downloads (Pure)

Abstract

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 language	English
Title of host publication	Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010
Editors	Yang Xiang, Pierangela Samarati, Jiankun Hu, Wanlei Zhou, Ahmad-Reza Sadeghi
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	318-322
Number of pages	5
ISBN (Print)	9780769541594
DOIs	https://doi.org/10.1109/NSS.2010.23
Publication status	Published - 2010
Event	4th International Conference on Network and System Security, NSS 2010 - Melbourne, VIC, Australia Duration: 1 Sept 2010 → 3 Sept 2010

Other

Other	4th International Conference on Network and System Security, NSS 2010
Country/Territory	Australia
City	Melbourne, VIC
Period	1/09/10 → 3/09/10

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 Universityâ€™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 [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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Cite this

Lu, L. F., Huang, M. L., Orgun, M. A., & Zhang, J. W. (2010). An improved wavelet analysis method for detecting DDoS attacks. In Y. Xiang, P. Samarati, J. Hu, W. Zhou, & A.-R. Sadeghi (Eds.), Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010 (pp. 318-322). Article 5635611 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/NSS.2010.23

Lu, Liang Fu ; Huang, Mao Lin ; Orgun, Mehmet A. et al. / An improved wavelet analysis method for detecting DDoS attacks. Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010. editor / Yang Xiang ; Pierangela Samarati ; Jiankun Hu ; Wanlei Zhou ; Ahmad-Reza Sadeghi. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2010. pp. 318-322

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abstract = "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.",

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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{\^a}€{\texttrademark}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 [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.; 4th International Conference on Network and System Security, NSS 2010 ; Conference date: 01-09-2010 Through 03-09-2010",

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Lu, LF, Huang, ML, Orgun, MA & Zhang, JW 2010, An improved wavelet analysis method for detecting DDoS attacks. in Y Xiang, P Samarati, J Hu, W Zhou & A-R Sadeghi (eds), Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010., 5635611, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 318-322, 4th International Conference on Network and System Security, NSS 2010, Melbourne, VIC, Australia, 1/09/10. https://doi.org/10.1109/NSS.2010.23

An improved wavelet analysis method for detecting DDoS attacks. / Lu, Liang Fu; Huang, Mao Lin; Orgun, Mehmet A. et al.
Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010. ed. / Yang Xiang; Pierangela Samarati; Jiankun Hu; Wanlei Zhou; Ahmad-Reza Sadeghi. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2010. p. 318-322 5635611.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

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AU - Lu, Liang Fu

AU - Huang, Mao Lin

AU - Orgun, Mehmet A.

AU - Zhang, Jia Wan

N1 - 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 Universityâ€™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 [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

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M3 - Conference proceeding contribution

AN - SCOPUS:78650317638

SN - 9780769541594

SP - 318

EP - 322

BT - Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010

A2 - Xiang, Yang

A2 - Samarati, Pierangela

A2 - Hu, Jiankun

A2 - Zhou, Wanlei

A2 - Sadeghi, Ahmad-Reza

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 4th International Conference on Network and System Security, NSS 2010

Y2 - 1 September 2010 through 3 September 2010

ER -

Lu LF, Huang ML, Orgun MA, Zhang JW. An improved wavelet analysis method for detecting DDoS attacks. In Xiang Y, Samarati P, Hu J, Zhou W, Sadeghi AR, editors, Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2010. p. 318-322. 5635611 doi: 10.1109/NSS.2010.23

An improved wavelet analysis method for detecting DDoS attacks

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