A Detection Algorithm to Anomaly Network Traffic Based on Spectral Kurtosis Analysis

Ding De Jiang; Cheng Yao; Wei Han Zhang; Zheng Zheng  Xu

doi:10.4028/www.scientific.net/AMR.765-767.1461

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767A Detection Algorithm to Anomaly Network Traffic...

A Detection Algorithm to Anomaly Network Traffic Based on Spectral Kurtosis Analysis

Abstract:

This paper presents a detection algorithm for anomaly network traffic, which is based on spectral kurtosis analysis. Firstly, we turn network traffic into time-frequency signals at different scales. These time-frequency signals hold the more detailed nature corresponding to different scales. Secondly, the time-frequency signals at different scales are transformed into a series of new time signals by time-frequency analysis theory. These new time signals hold obvious narrowband nature and embody the local properties of network traffic. Thirdly, we calculate the spectral kurtosis values of the new time signals and then perform the feature extractions. As a result, the abnormal network traffic can be correctly identified. Simulation results show that our algorithm is feasible and promising.

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Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

1461-1464

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.765-767.1461

Citation:

Cite this paper

Online since:

September 2013

Authors:

Ding De Jiang, Cheng Yao, Wei Han Zhang, Zheng Zheng Xu

Keywords:

Anomaly Detection, Network Traffic, Spectral Kurtosis, Time-Frequency Analysis

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References

[1] V. Chandola, A. Banerjee, V. Kumar. Anomaly detection for discrete sequences: A survey. IEEE Transactions on Knowledge and Data Engineering, 24(5): 823-839, (2012).

DOI: 10.1109/tkde.2010.235

Google Scholar

[2] D. Jiang, Z. Xu, Z. Chen, et al., Joint time-frequency sparse estimation of large-scale network traffic, Computer Networks, 55(10): 3533-3547, (2011).

DOI: 10.1016/j.comnet.2011.06.027

Google Scholar

[3] S. Federico, I. A. Juan, C. Pablo, et al., Anomaly detection in network traffic based on statistical inference and α-stable modeling, IEEE Transactions on Dependable and Secure Computing, 8(4): 494-509, (2011).

DOI: 10.1109/tdsc.2011.14

Google Scholar

[4] L. Guo, J. Cao, H Yu, L. Li. Path-based routing provisioning with mixed shared protection in WDM mesh networks, Journal of Lightwave Technology, 2006, 24 (3): 1129-1141.

DOI: 10.1109/jlt.2005.863249

Google Scholar

[5] L. Guo. LSSP: A novel local segment-shared protection for multi-domain optical mesh networks, Computer Communications, 2007, 30 (8): 1794-1801.

DOI: 10.1016/j.comcom.2007.02.010

Google Scholar

[6] T. Qin, X. Guan, W. Li, et al., Monitoring abnormal network traffic based on blind source separation approach, Journal of Network and Computer Applications, 34 (2011): 1732-1742, (2011).

DOI: 10.1016/j.jnca.2011.06.006

Google Scholar

[7] D. Jiang, G. Hu, GARCH model-based large-scale IP traffic matrix estimation, IEEE Communications Letters, 13(1): 52-54, (2009).

DOI: 10.1109/lcomm.2008.081271

Google Scholar

[8] T. Akgül, S. Baykut, M. E. Kantarci, et al., Periodicity-based anomalies in self-similar network traffic flow measurements, IEEE Transactions on Instrumentation and Measurement, 60(4): 1358-1366, (2011).

DOI: 10.1109/tim.2010.2084711

Google Scholar

[9] G. Thatte, U. Mitra, J. Heidemann, Parametric methods for anomaly detection in aggregate traffic, IEEE Transactions on Networking, 19(2): 512-525, (2011).

DOI: 10.1109/tnet.2010.2070845

Google Scholar

[10] M. Celenk, T. Conley, J. Willis, et al., Predictive network anomaly detection and visualization, IEEE Transactions on Information Forensics and Security, 5(2): 288-299, (2010).

DOI: 10.1109/tifs.2010.2041808

Google Scholar

[11] P. Chhabra, C. Scott, E. Kolaczyk, et al., Distributed spatial anomaly detection, in Proc. INFOCOM, Phoenix, AZ, pp.2378-2386, (2008).

Google Scholar

[12] X. Lijun. Cancellation of harmonic interference by baseline shifting of wavelet packet decomposition coefficients. IEEE Transactions on Signal Processing, 53(1): 222-230, (2005).

DOI: 10.1109/tsp.2004.838954

Google Scholar

[13] F. Millioz, N. Martin. Circularity of the STFT and spectral kurtosis for time-frequency segmentation in Gaussian environment. IEEE Transactions on Signal Processing, 59(2): 515-524, (2011).

DOI: 10.1109/tsp.2010.2081986

Google Scholar