Extracting Features from Both the Input and the Output of a Convolutional Neural Network to Detect Distributed Denial of Service Attacks

Abstract

Distributed Denial-of-Service (DDoS) attacks are serious threats to a smart grid infrastructure services' availability, and 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). An improved version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the non-pure fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract distinguishing features during data pre-processing. A support vector machine (SVM) was used for post data-processing. The implementation detected the DDoS attack with 87.35% accuracy.