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A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City

Latifah Almuqren¹, Sumayh S Aljameel², Hamed Alqahtani³

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

Sensors (Basel, Switzerland)

|September 9, 2023

Summary

This study introduces a novel cybersecurity solution for smart grids to detect Distributed Denial of Service (DDoS) attacks. The White Shark Equilibrium Optimizer with Hybrid Deep Learning (WSEO-HDLCS) effectively identifies and mitigates these threats in smart city environments.

Keywords:

DDoS attacks cybersecurity deep autoencoder feature selection smart cities smart grids

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Area of Science:

Cybersecurity
Smart Grids
Artificial Intelligence

Background:

Smart grids are crucial for smart cities, managing energy efficiently.
Distributed Denial of Service (DDoS) attacks pose a significant cybersecurity threat to smart grid stability.
Existing methods struggle with the complexity and volume of data in smart grid cybersecurity.

Purpose of the Study:

To develop an advanced cybersecurity solution for smart grids to detect and mitigate DDoS attacks.
To enhance the reliability and stability of smart grids within smart city infrastructures.
To address the challenge of high-dimensionality data in identifying cyber threats.

Main Methods:

A novel White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) is proposed.
WSEO-based feature selection (WSEO-FS) is employed to handle high-dimensional data.
A stacked deep autoencoder (SDAE) model is utilized for DDoS attack detection, with hyperparameters optimized by the Gravitational Search Algorithm (GSA).

Main Results:

The WSEO-HDLCS technique effectively identifies the presence of DDoS attacks in smart grids.
The WSEO-FS approach successfully resolves high-dimensionality data challenges.
Simulations on the CICIDS-2017 dataset demonstrated superior performance compared to existing methodologies.

Conclusions:

The WSEO-HDLCS technique offers a promising and effective solution for smart grid cybersecurity against DDoS attacks.
This approach enhances the security and reliability of smart grids in smart city environments.
The study highlights the potential of hybrid deep learning and optimization algorithms for advanced cyber threat detection.