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DDoS attack detection in Edge-IIoT digital twin environment using deep learning approach.

Feras Al-Obeidat¹, Adnan Amin², Ahmed Shuhaiber¹

¹College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates.

Peerj. Computer Science

|September 24, 2025

Summary

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This study presents a new method for detecting distributed denial of service (DDoS) attacks in Edge-IIoT digital twins. The approach effectively transfers knowledge between models, outperforming existing methods without full retraining.

Area of Science:

Cybersecurity
Industrial Internet of Things (IIoT)
Digital Twins

Background:

The Industrial Internet of Things (IIoT) and digital twins are transforming industrial systems.
Edge-IIoT growth necessitates robust security measures against threats like distributed denial of service (DDoS) attacks.
DDoS attacks exploit botnets to overwhelm systems with traffic.

Purpose of the Study:

To introduce a novel approach for detecting DDoS attacks within an Edge-IIoT digital twin environment.
To develop a method that retains learned knowledge and adapts to new models continuously without complete retraining.
To evaluate the proposed approach on a publicly available dataset.

Main Methods:

A novel DDoS attack detection approach was developed for Edge-IIoT digital twins.

Keywords:

DDoS attacks Deep learning Digital twins Edge-IIoT

Related Experiment Videos

The method focuses on continuous learning, adapting to new models without full retraining.

The approach was tested on a dataset of 157,600 samples.

Main Results:

The proposed models (M1, M2, M3) achieved high performance metrics: precision (0.93-0.94), recall (0.91-0.99), F1-score (0.93-0.96), and accuracy (0.93-0.96).
Knowledge transfer between sequential models consistently surpassed baseline methods.
The approach demonstrated effective DDoS attack detection in the Edge-IIoT digital twin context.

Conclusions:

The proposed knowledge-transferring approach is effective for DDoS attack detection in Edge-IIoT digital twins.
Continuous adaptation without retraining is a viable strategy for enhancing security models.
The findings highlight the potential of digital twins in securing IIoT environments.