An Efficient DoS Attacks Detection Method Based on Data Mining Scheme

Xiang Chen

doi:10.4028/www.scientific.net/AMR.267.302

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 267An Efficient DoS Attacks Detection Method Based on...

An Efficient DoS Attacks Detection Method Based on Data Mining Scheme

Abstract:

To defend against DoS attacks and ensure QoS of web server, we first propose an efficient network anomaly detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm. Secondly, we integrate a lot of objective and efficient DoS impact metrics from the perceptions of the end users into TCM-KNN algorithm to build a robust anomaly detection mechanism. Finally, Genetic Algorithm (GA) based instance selection method is introduced to boost the real-time detection performance of our method.

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Periodical:

Advanced Materials Research (Volume 267)

Pages:

302-307

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.267.302

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Online since:

June 2011

Authors:

Xiang Chen

Keywords:

DoS Detection, Quality of Service (QoS), TCM-KNN, Web Server

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References

[1] E. ikovic, P. iher, O. ahmy, and R. homas, Measuring Denial of Service, " in Proc. ACM QoP, 06, pp.53-58, (2006).

Google Scholar

[2] Y. Li, B.X. Fang, L. Guo, and Y. Chen, Network Anomaly Detection Based on TCM-KNN Algorithm, in Proc. ACM ASIACCS 2007, pp.13-19.

DOI: 10.1145/1229285.1229292

Google Scholar

[3] M. Li, and P. Vitanyi, Introduction to Kolmogorov Complexity and its Applications. Springer Verlag, (1997).

Google Scholar

[4] K. Proedru, I. Nouretdinov, V. Vovk, and A. Gammerman, Transductive confidence machine for pattern recognition, in Proc. 13th European conference on Machine Learning, pp.381-390, (2002).

DOI: 10.1007/3-540-36755-1_32

Google Scholar

[5] J. Laetitia, D. Clarisse, and T. Ghazali, A Genetic Algorithm for Feature Selection in Data-Mining for Genetics, in Proc. of 4th metaheuristics international conference, pp.29-33, (2001).

Google Scholar

[6] R. Jose, H. Francisco, and L. Manuel, Using Evolutionary Algorithms as Instance Selection for Data Reduction in KDD: An Experimental Study, IEEE transactions on evolutionary computation, pp.561-575, (2006).

DOI: 10.1109/tevc.2003.819265

Google Scholar