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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection

Mimouna Abdullah Alkhonaini1

  • 1Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia. mkhonaini@psu.edu.sa.

Scientific Reports
|August 6, 2025
PubMed
Summary

This study introduces a Fox Optimizer-Based Feature Selection with Deep Learning for Securing Cyberattack Detection (FOFSDL-SCD) model to enhance Internet of Things (IoT) cybersecurity. The novel approach significantly improves threat detection accuracy and resilience in smart city networks.

Keywords:
Cyberattack detectionCybersecurityData Pre-processingDeep learningDung beetle optimizationFeature selectionIoT

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • Smart cities leverage Internet of Things (IoT) devices for enhanced efficiency and sustainability.
  • The proliferation of IoT in smart cities introduces critical cybersecurity challenges, including data breaches and unauthorized access.
  • Existing cybersecurity measures require advanced techniques to effectively safeguard interconnected IoT systems.

Purpose of the Study:

  • To develop and evaluate a novel model for enhancing cybersecurity in IoT networks.
  • To improve the resilience and threat detection capabilities of smart city infrastructure against cyberattacks.
  • To analyze advanced deep learning techniques for intelligent cybersecurity solutions.

Main Methods:

  • A Fox Optimizer-Based Feature Selection with Deep Learning for Securing Cyberattack Detection (FOFSDL-SCD) model was proposed.
  • Data pre-processing involved min-max normalization; feature selection utilized the Fox Optimizer Algorithm (FOA).
  • Classification was performed using a Temporal Convolutional Network (TCN) with hyperparameters optimized by Dung Beetle Optimization (DBO).

Main Results:

  • The FOFSDL-SCD model achieved superior performance on the Edge-IIoT dataset.
  • The model demonstrated high accuracy (99.38%), precision (96.27%), recall (96.26%), and F1-Score (96.27%).
  • Comparative analysis confirmed the FOFSDL-SCD approach outperformed existing cybersecurity models.

Conclusions:

  • The FOFSDL-SCD model offers a robust and effective solution for detecting cyberattacks in IoT environments.
  • The integration of Fox Optimizer and Deep Learning significantly enhances IoT network security.
  • This research contributes to advancing intelligent cybersecurity strategies for smart cities.