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Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

Maryam Ghanbari, Witold Kinsner

Source Title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)14(1)

ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799805311|DOI: 10.4018/IJCINI.2020010102

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MLA

Ghanbari, Maryam, and Witold Kinsner. "Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning." IJCINI vol.14, no.1 2020: pp.17-34. http://doi.org/10.4018/IJCINI.2020010102

APA

Ghanbari, M. & Kinsner, W. (2020). Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(1), 17-34. http://doi.org/10.4018/IJCINI.2020010102

Chicago

Ghanbari, Maryam, and Witold Kinsner. "Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 14, no.1: 17-34. http://doi.org/10.4018/IJCINI.2020010102

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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.