Smart network security using advanced ensemble-DDoS attack detection and hybrid JA-SLOA-linked optimal routing-based mitigation

Abstract

ABSTRACT Distributed Denial of Service (DDoS) attacks are distributed at a faster rate, and they are considered to be fatal threats over the Internet. Moreover, several deep learning approaches are insufficient to attain the maximum efficiency and appropriate detection due to the complexity and diversity of DDoS attack traffic under fast fast-speed network environment since they are providing with individual performance. Hence, an ensemble learning model is developed for DDoS detection and mitigation with the hybrid optimization algorithm to ensure good detective performance against DDoS attacks. The pre-processed data is fed to the feature extraction process where it is done through the Deep Belief Network (DBN) and Autoencoder techniques for acquiring the deep features. The optimized fusion of deep features takes place using hybrid meta-heuristic algorithm of Adaptive Sound Speed-based Jaya Sea Lion Optimization (ASS-JSLnO). The attack detection is performed by the Improved Ensemble Learning (IEL) approach. Then, the mitigation strategy is applied by the optimal routing and multi-objective function. Finally, the experiments are made to establish the high detection efficacy of the implemented attack detection and mitigation framework.