Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39656
Title: Fractional gaussian noise: A tool of characterizing traffic for detection purpose
Authors: Li, M. 
Chi, H. 
Long, D.
Issue Date: 2004
Source: Li, M.,Chi, H.,Long, D. (2004). Fractional gaussian noise: A tool of characterizing traffic for detection purpose. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3309 : 94-103. ScholarBank@NUS Repository.
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. © Springer-Verlag 2004.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/39656
ISSN: 03029743
Appears in Collections:Staff Publications

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