Document Type

Article

Source of Publication

Peerj Computer Science

Publication Date

8-29-2025

Abstract

The industrial Internet of Things (IIoT) and digital twins are redefining how digital models and physical systems interact. IIoT connects physical intelligence, and digital twins virtually represent their physical counterparts. With the rapid growth of Edge-IIoT, it is crucial to create security and privacy regulations to prevent vulnerabilities and threats (i.e., distributed denial of service (DDoS)). DDoS attacks use botnets to overload the target system with requests. In this study, we introduce a novel approach for detecting DDoS attacks in an Edge-IIoT digital twin-based generated dataset. The proposed approach is designed to retain already learned knowledge and easily adapt to new models in a continuous manner without retraining the deep learning model. The target dataset is publicly available and contains 157,600 samples. The proposed models M1, M2, and M3 obtained precision scores of 0.94, 0.93, and 0.93; recall scores of 0.91, 0.97, and 0.99; F1-scores of 0.93, 0.95, and 0.96; and accuracy scores of 0.93, 0.95, and 0.96, respectively. The results demonstrated that transferring previous model knowledge to the next model consistently outperformed baseline approaches.

ISSN

2376-5992

Publisher

PeerJ

Volume

11

Disciplines

Computer Sciences

Keywords

Edge-IIoT, Deep learning, DDoS attacks, Digital twins

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

no

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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