Abstract
Detecting signs of distributed denial-of-service (DDOS) flood attacks based on traffic time series analysis needs characterizing traffic series using a statistical model. The essential thing about this model should consistently characterize various types of traffic (such as TCP, UDP, IP, and OTHER) in the same order of magnitude of modeling accuracy. Our previous work [1] uses fractional Gaussian noise (FGN) as a tool for featuring traffic series for the purpose of reliable detection of signs of DDOS flood attacks. As a supplement of [1], this article gives experimental investigations to show that FGN can yet be used for modeling autocorrelation functions of various types network traffic (TCP, UDP, IP, OTHER) consistently in the sense that the modeling accuracy (expressed by mean square error) is in the order of magnitude of 10− − 3.
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References
Li, M.: An approach for reliably identifying signs of DDOS flood attacks based on LRD traffic pattern recognition. Computer & Security 23(6) (2004)
Mandelbrot, B.B.: Gaussian Self-Affinity and Fractals. Springer, Heidelberg (2001)
Beran, J.: Statistics for Long-Memory Processes. Chapman & Hall, Boca Raton (1994)
Willinger, W., Taqqu, M.S., Leland, W.E., Wilson, D.V.: Self-similarity in high-speed packet traffic: Analysis and modeling of Ethernet traffic measurements. Statistical Science 10(10), 67–85 (1995)
Willinger, W., Paxson, V.: Where mathematics meets the Internet. Notices of the American Mathematical Society 45(8), 961–970 (1998)
Adas, A.: Traffic models in broadband networks. IEEE Communications Magazine 35(7), 82–89 (1997)
Li, M., Jia, W., Zhao, W.: Correlation form of timestamp increment sequences of selfsimilar traffic on Ethernet. Electronics Letters 36(19), 1168–1169 (2000)
Li, M., Chi, C.-H.: A correlation based computational model for synthesizing long-range dependent data. J. Franklin Institute 340(6-7), 503–514 (2003)
Michiel, H., Laevens, K.: Teletraffic engineering in a broad-band era. Proc. of the IEEE 85(12), 2007–2033 (1997)
Paxson, V., Floyd, S.: Wide area traffic: The failure of Poison modeling. IEEE/ACM Trans. on Networking 3(3), 226–244 (1995)
Tsybakov, B., Georganas, N.D.: Self-similar processes in communications networks. IEEE Trans. on Information Theory 44(5), 1713–1725 (1998)
Li, M., Zhao, W., Long, D.Y., Chi, C.-H.: Modeling autocorrelation functions of selfsimilar teletraffic in communication networks based on optimal approximation in Hilbert space. Applied Mathematical Modelling 27(3), 155–168 (2003)
Paxson, V.: Fast, approximate synthesis of fractional Gaussian noise for generating selfsimilar network traffic. Computer Comm. Review 27(5), 5–18 (1997)
Aubin, J.P.: Applied Functional Analysis, 2nd edn. John Wiley & Sons, Chichester (2000)
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© 2004 Springer-Verlag Berlin Heidelberg
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Li, M., Chi, CH., Long, D. (2004). Fractional Gaussian Noise: A Tool of Characterizing Traffic for Detection Purpose. In: Chi, CH., Lam, KY. (eds) Content Computing. AWCC 2004. Lecture Notes in Computer Science, vol 3309. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30483-8_12
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DOI: https://doi.org/10.1007/978-3-540-30483-8_12
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