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Zero-Crossing Analysis and Information Divergence of Lévy Walks for Real-Time Feature Extraction

Zero-Crossing Analysis and Information Divergence of Lévy Walks for Real-Time Feature Extraction

Jesus David Terrazas Gonzalez, Witold Kinsner
Copyright: © 2016 |Volume: 7 |Issue: 4 |Pages: 19
ISSN: 1947-9158|EISSN: 1947-9166|EISBN13: 9781466691476|DOI: 10.4018/IJHCR.2016100104
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MLA

Gonzalez, Jesus David Terrazas, and Witold Kinsner. "Zero-Crossing Analysis and Information Divergence of Lévy Walks for Real-Time Feature Extraction." IJHCR vol.7, no.4 2016: pp.41-59. http://doi.org/10.4018/IJHCR.2016100104

APA

Gonzalez, J. D. & Kinsner, W. (2016). Zero-Crossing Analysis and Information Divergence of Lévy Walks for Real-Time Feature Extraction. International Journal of Handheld Computing Research (IJHCR), 7(4), 41-59. http://doi.org/10.4018/IJHCR.2016100104

Chicago

Gonzalez, Jesus David Terrazas, and Witold Kinsner. "Zero-Crossing Analysis and Information Divergence of Lévy Walks for Real-Time Feature Extraction," International Journal of Handheld Computing Research (IJHCR) 7, no.4: 41-59. http://doi.org/10.4018/IJHCR.2016100104

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Abstract

A method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Lévy-walks is presented. This method can be utilized for generating arbitrary prescribed probability density functions (pdf). A cybersecurity engineering problem associated with Internet traffic is addressed. The synthetic Lévy-walks process is intertwined with sections of distinct characteristics creating a composite signal that is analyzed through zero-crossing rate (ZCR) within a varying-size window to identify sections. The advantages of the ZCR computation directly in the time-domain are appealing for real-time implementations. Moreover, the characterization of the degree of closeness, via the Kullback-Leibler divergence (KLD), among the pdfs of arbitrary processes (focusing on Lévy walks) and model pdfs is presented. The results obtained from the KLD experiments provide a categorical determination of the closeness degree. These results, a remarkable achievement in this research, are also promising to be used as features for classification of complex signals in real-time.

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