Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei
Abstract
The increasing threat of Distributed DDoS attacks necessitates robust, big data-driven methods to detect and mitigate complex Network and Transport Layer (NTL) attacks. This paper proposes EffiGRU-GhostNet, a deep-learning ensemble model for high-accuracy DDoS detection with minimal resource consumption. EffiGRU-GhostNet integrates Gated Recurrent Units (GRU) with the GhostNet architecture, optimized through Principal Component Analysis with Locality Preserving Projections (PCA-LLP) to handle large-scale data effectively. Our ensemble was tested on IoT-23, APA-DDoS, and additional datasets created from popular DDoS attack tools. Simulations demonstrate a recognition rate of 98.99% on IoT-23 with a 0.11% false positive rate and 99.05% accuracy with a 0.01% error on APA-DDoS, outperforming SVM, ANN-GWO, GRU-RNN, CNN, LSTM, and DBN baselines. Statistical validation through Wilcoxon and Spearman’s tests further verifies EffiGRU-GhostNet’s effectiveness across datasets, with a Wilcoxon F-statistic of 7.632 (p = 0.022) and a Spearman correlation of 0.822 (p = 0.005). This study demonstrates that EffiGRU-GhostNet is a reliable, scalable solution for dynamic DDoS detection, advancing the field of big data-driven cybersecurity.