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. 2024 Dec 28;14(1):30868.
doi: 10.1038/s41598-024-81116-4.

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

Chukka Ramesh Babu  1 , M Suneetha  2 , Mohammed Altaf Ahmed  3 , Palamakula Ramesh Babu  4 , Mohamad Khairi Ishak  5 , Hend Khalid Alkahtani  6 , Samih M Mostafa  7   8
Affiliations

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

Chukka Ramesh Babu et al. Sci Rep. .

Erratum in

Abstract

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.

Keywords: Deep learning; Distributed denial of service; Graph Convolutional Network; Internet of things; Northern Goshawk optimization; Synergistic Swarm optimization.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Conflict of interest: The authors declare that they have no conflict of interest. The manuscript was written with the contributions of all authors, and all authors have approved the final version. Ethics approval: This article contains no studies with human participants performed by any authors. Consent to participate: Not applicable. Informed consent: Not applicable.

Figures

Fig. 1
Fig. 1
General structure of DDoS attack in IoT.
Fig. 2
Fig. 2
Overall process of SSODE-GCNDM technique.
Fig. 3
Fig. 3
Structure of the SSO-DE model.
Fig. 4
Fig. 4
Structure of GCN.
Fig. 5
Fig. 5
Architecture of the NGO approach.
Fig. 6
Fig. 6
Confusion matrices of SSODE-GCNDM technique (a-f) Epochs 500–3000.
Fig. 7
Fig. 7
Average outcome of SSODE-GCNDM technique (a) Epochs 500, (b) Epochs 1000, (c) Epochs 1500, (d) Epochs 2000, (e) Epochs 2500, (f) Epochs 3000.
Fig. 8
Fig. 8
formula image curve of SSODE-GCNDM technique on Epoch 1000.
Fig. 9
Fig. 9
Loss curve of SSODE-GCNDM technique on Epoch 1000.
Fig. 10
Fig. 10
PR curve of SSODE-GCNDM technique on Epoch 1000.
Fig. 11
Fig. 11
ROC curve of SSODE-GCNDM technique on Epoch 1000.
Fig. 12
Fig. 12
Comparative analysis of SSODE-GCNDM approach with recent models.
Fig. 13
Fig. 13
CT outcome of SSODE-GCNDM technique with recent models.
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