Designing a Neural Network and a Genetic- Algorithm-Based Adaptive Wavelet for Internet Traffic Containing DDoS Attacks

This paper presents the design of an adaptive mother wavelet for detecting Internet traffic data (ITD) with distributed denial of service (DDoS) attacks (DDoS ITD). The proposed procedure consists of designing an adaptive mother wavelet genetic neural network (GNN) for detecting the DDoS ITD, A multi-objective optimization based on a genetic algorithm is used to create a set of adaptive mother wavelets that best fit the weight parameters for a given input data recording. Moreover, a weighted cost function is used to measure how well the GNN is able to create a mother wavelet. The best mother wavelet coefficients for detecting DDoS attacks are achieved with coefficients [−0.3744,0.0034]. The created mother wavelet increased the detection rate of the DDoS attacks by 0.3% when compared to the Haar mother wavelet.