Hybridization of synergistic swarm and differential evolution with graph convolutional network for distributed denial of service detection and mitigation in IoT environment

Enhanced technologies of the future are gradually improving the digital landscape. Internet of Things (IoT) technology is an advanced technique that is quickly increasing owing to the development of a network of organized online devices. In today's digital era, the IoT is considered one of the most robust technologies. However, attackers can effortlessly hack the IoT devices employed to generate botnets, and it is applied to present distributed denial of service (DDoS) attacks beside networks. The DDoS attack is the foremost attack on the system that causes the complete network to go down. Thus, average consumers may need help to get the services they need from the server. The compromised or attackers IoT devices want to be perceived well in the system. So, presently, Deep Learning (DL) plays a prominent part in forecasting end-users' behaviour by extracting features and identifying the adversary in the network. This paper proposes a Synergistic Swarm Optimization and Differential Evolution with Graph Convolutional Network Cyberattack Detection and Mitigation (SSODE-GCNDM) technique in the IoT environment. The main intention of the SSODE-GCNDM method is to recognize the presence of DDoS attack behaviour in IoT platforms. Primarily, the SSODE-GCNDM technique utilizes Z-score normalization to scale the input data into a uniform format. The presented SSODE-GCNDM approach utilizes synergistic swarm optimization with a differential evolution (SSO-DE) approach for the feature selection. Moreover, the graph convolutional network (GCN) method recognizes and mitigates attacks. Finally, the presented SSODE-GCNDM technique implements the northern goshawk optimization (NGO) method to fine-tune the hyperparameters involved in the GCN method. An extensive range of experimentation analyses occur, and the outcomes are observed using numerous features. The experimental validation of the SSODE-GCNDM technique portrayed a superior accuracy value of 99.62% compared to existing approaches.