Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

Maryam Ghanbari and Witold Kinsner
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Maryam Ghanbari: University of Manitoba, Winnipeg, Canada
Witold Kinsner: University of Manitoba, Winnipeg, Canada

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2020, vol. 14, issue 1, 17-34

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.

Date: 2020
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