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Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

Maryam Ghanbari, Witold Kinsner
Copyright: © 2022 |Pages: 19
ISBN13: 9781668436660|ISBN10: 1668436663|EISBN13: 9781668436677
DOI: 10.4018/978-1-6684-3666-0.ch048
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MLA

Ghanbari, Maryam, and Witold Kinsner. "Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning." Research Anthology on Smart Grid and Microgrid Development, edited by Information Resources Management Association, IGI Global, 2022, pp. 1078-1096. https://doi.org/10.4018/978-1-6684-3666-0.ch048

APA

Ghanbari, M. & Kinsner, W. (2022). Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning. In I. Management Association (Ed.), Research Anthology on Smart Grid and Microgrid Development (pp. 1078-1096). IGI Global. https://doi.org/10.4018/978-1-6684-3666-0.ch048

Chicago

Ghanbari, Maryam, and Witold Kinsner. "Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning." In Research Anthology on Smart Grid and Microgrid Development, edited by Information Resources Management Association, 1078-1096. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-3666-0.ch048

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Abstract

Distributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract significant distinguishing features during data pre-processing. A support vector machine (SVM) was used for data post-processing. The implementation detected the DDoS attack with 87.35% accuracy.

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