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Leveraging hybrid deep learning with starfish optimization algorithm based secure mechanism for intelligent edge

Amal K Alkhalifa¹, Mohammed Aljebreen², Rakan Alanazi³

¹Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Scientific Reports

|September 26, 2025

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This summary is machine-generated.

This study introduces a Hybrid Deep Learning-Based Intrusion Detection for Edge Computing Using Starfish Optimization Algorithm (HDLID-ECSOA) to secure Internet of Things (IoT) devices. The novel technique achieves over 99% accuracy in detecting threats in smart city environments.

Area of Science:

Cybersecurity
Artificial Intelligence
Edge Computing

Background:

The proliferation of Internet of Things (IoT) devices across various domains, including smart cities, has amplified security concerns.
Edge computing offers solutions for latency-sensitive computational processing of large data volumes generated by IoT applications.
Integrating AI and deep learning methods is crucial for developing effective Intrusion Detection Systems (IDS) for IoT environments.

Purpose of the Study:

To propose a novel Hybrid Deep Learning-Based Intrusion Detection for Edge Computing Using Starfish Optimization Algorithm (HDLID-ECSOA) technique.
To enhance the security of intelligent edge computing within smart city infrastructures.
To leverage advanced optimization models for improved IoT security.

Main Methods:

Keywords:

Deep learning Dingo optimizer algorithm Edge computing Intrusion detection Starfish optimization algorithm

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Data pre-processing using min-max normalization for data standardization.
Feature selection employing the Dingo Optimizer Algorithm (DOA).
Intrusion classification using a hybrid Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU) with a Cross-Attention Mechanism (CrAM).
Hyperparameter tuning via the Starfish Optimization Algorithm (SFOA) for optimal model performance.

Main Results:

The HDLID-ECSOA model demonstrated superior performance on the Edge-IIoT and ToN-IoT datasets.
Achieved high accuracy rates of 99.35% and 99.33% respectively, outperforming existing intrusion detection techniques.
The proposed method effectively identifies and classifies intrusions in edge computing environments.

Conclusions:

The HDLID-ECSOA technique provides an effective and accurate solution for intrusion detection in edge computing for IoT.
The integration of deep learning and advanced optimization algorithms significantly enhances cybersecurity for smart city applications.
The study validates the potential of AI-driven security models in safeguarding complex IoT ecosystems.