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A Hilbert–Huang transform approach for predicting cyber-attacks

Authors
Journal
Journal of the Korean Statistical Society
1226-3192
Publisher
Elsevier
Publication Date
Volume
37
Issue
3
Identifiers
DOI: 10.1016/j.jkss.2008.02.006
Keywords
  • Primary
  • Secondary
  • Cyber-Attack
  • Decomposition
  • Hilbert Spectrum
  • Hilbert–Huang Transform
  • Prediction
  • Vector Ar Process
Disciplines
  • Design

Abstract

Abstract A statistical method for prediction and modeling of cyber-attack signal is proposed. The proposed method is developed by coupling the traditional ARIMA method with Hilbert–Huang transform (HHT), designed to reduce the dimensionality and to extract meaningful signals for reliable prediction. HHT decomposes cyber-attack signals of interest into several components including short- and long-term patterns, and random fluctuation. Due to Hilbert transform, the method selects significant decomposed signals that will be employed for signal prediction. Subsequently, by using the traditional dynamic models, the proposed method provides a stable prediction of cyber-attack signal. To show the performance of the proposed method, we analyze daily worm count data from August 1, 2005 to October 9, 2006.

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