Detection of ICMPv6 DDoS Attacks using Hybridization of RNN and GRU

The internet, crucial for information exchange, operates on IPv6 and IPv4 protocols, which are vulnerable to DDoS attacks. Despite secure-edge advancements, these attacks still cause significant losses. This paper presents a Deep Neural Network (DNN) architecture to address these vulnerabilities. Model 1 integrates Recurrent Neural Networks (RNN) with Gated Recurrent Units (GRU), inspired by Ahmed Issa, while Model 2 employs Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM). These models were tested on Mendeley, NSL-KDD, and Sain Malaysian datasets, achieving accuracies of 80%, 80% 97.01%, 95.06%, 72.89%, and 64.94%, respectively. The objective is to verify the practical feasibility of these combinations to detect DDoS-attacks. The same architecture was implemented in Model 1 for further evaluation using NSL-KDD as used by Issa, Mendeley IPv4, and Sain Malaysian datasets. A new ICMPv6 datasets were deployed with different architecture layers on the proposed model resulting in promising accuracies of 99.36% and 94.48%.