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Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning
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Author(s): Maryam Ghanbari (University of Manitoba, Winnipeg, Canada) and Witold Kinsner (Cognitive Systems Laboratory, Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada & Telecommunications Research Laboratories (TRLabs), Winnipeg, Canada)
Copyright: 2020
Volume: 14
Issue: 1
Pages: 18
Source title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)
Editor(s)-in-Chief: Kangshun Li (South China Agricultural University, China)
DOI: 10.4018/IJCINI.2020010102


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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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