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Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction: Composite and Applied Signal Analysis for Strengthening the Internet-of-Things Against DDoS Attacks

Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction: Composite and Applied Signal Analysis for Strengthening the Internet-of-Things Against DDoS Attacks

Jesus David Terrazas Gonzalez, Witold Kinsner
Copyright: © 2021 |Pages: 27
ISBN13: 9781799853480|ISBN10: 1799853489|EISBN13: 9781799853497
DOI: 10.4018/978-1-7998-5348-0.ch021
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MLA

Gonzalez, Jesus David Terrazas, and Witold Kinsner. "Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction: Composite and Applied Signal Analysis for Strengthening the Internet-of-Things Against DDoS Attacks." Research Anthology on Combating Denial-of-Service Attacks, edited by Information Resources Management Association, IGI Global, 2021, pp. 388-414. https://doi.org/10.4018/978-1-7998-5348-0.ch021

APA

Gonzalez, J. D. & Kinsner, W. (2021). Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction: Composite and Applied Signal Analysis for Strengthening the Internet-of-Things Against DDoS Attacks. In I. Management Association (Ed.), Research Anthology on Combating Denial-of-Service Attacks (pp. 388-414). IGI Global. https://doi.org/10.4018/978-1-7998-5348-0.ch021

Chicago

Gonzalez, Jesus David Terrazas, and Witold Kinsner. "Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction: Composite and Applied Signal Analysis for Strengthening the Internet-of-Things Against DDoS Attacks." In Research Anthology on Combating Denial-of-Service Attacks, edited by Information Resources Management Association, 388-414. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5348-0.ch021

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Abstract

A comparison between the probability similarities of a Distributed Denial-of-Service (DDoS) dataset and Lévy walks is presented. This effort validates Lévy walks as a model resembling DDoS probability features. In addition, a method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Lévy-walks is demonstrated. The Smirnov transform is used to address a cybersecurity problem associated with the Internet-of-things (IoT). The synthetic Lévy-walk is merged with sections of distinct signals (uniform noise, Gaussian noise, and an ordinary sinusoid). Zero-crossing rate (ZCR) within a varying-size window is utilized to analyze both the composite signal and the DDoS dataset. ZCR identifies all the distinct sections in the composite signal and successfully detects the occurrence of the cyberattack. The ZCR value increases as the signal under analysis becomes more complex and produces steadier values as the varying window size increases. The ZCR computation directly in the time-domain is its most notorious advantage for real-time implementations.

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