Open Access

ARTICLE

Boosting Cybersecurity: A Zero-Day Attack Detection Approach Using Equilibrium Optimiser with Deep Learning Model

Mona Almofarreh¹, Amnah Alshahrani², Nouf Helal Alharbi³, Ahmed Omer Ahmed⁴, Hussain Alshahrani⁵, Abdulrahman Alzahrani^6,*, Mohammed Mujib Alshahrani⁷, Asma A. Alhashmi⁸

1 Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science and Information, Applied College, Taibah University, Tayba, Madinah, 42353, Saudi Arabia
4 Department of Information Systems, Applied College at Mahayil, King Khalid University, Abha, 62529, Saudi Arabia
5 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, 11961, Saudi Arabia
6 Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, Al Jamiah, Hafar Al Batin, 39524, Saudi Arabia
7 Department of Information Systems and Cybersecurity, College of Computing and Information Technology, University of Bisha, Bisha, P.O. Box 551, Saudi Arabia
8 Department of Computer Science, College of Science, Northern Border University, Arar, 73213, Saudi Arabia

* Corresponding Author: Abdulrahman Alzahrani. Email:

Computer Modeling in Engineering & Sciences 2025, 145(2), 2631-2656. https://doi.org/10.32604/cmes.2025.070545

Received 18 July 2025; Accepted 28 September 2025; Issue published 26 November 2025

Abstract

Zero-day attacks use unknown vulnerabilities that prevent being identified by cybersecurity detection tools. This study indicates that zero-day attacks have a significant impact on computer security. A conventional signature-based detection algorithm is not efficient at recognizing zero-day attacks, as the signatures of zero-day attacks are usually not previously accessible. A machine learning (ML)-based detection algorithm is proficient in capturing statistical features of attacks and, therefore, optimistic for zero-day attack detection. ML and deep learning (DL) are employed for designing intrusion detection systems. The improvement of absolute varieties of novel cyberattacks poses significant challenges for IDS solutions that are dependent on datasets of prior signatures of the attacks. This manuscript presents the Zero-day attack detection employing an equilibrium optimizer with a deep learning (ZDAD-EODL) method to ensure cybersecurity. The ZDAD-EODL technique employs meta-heuristic feature subset selection using an optimum DL-based classification technique for zero-day attacks. Initially, the min-max scalar is utilized for normalizing the input data. For feature selection (FS), the ZDAD-EODL method utilizes the equilibrium optimizer (EO) model to choose feature sub-sets. In addition, the ZDAD-EODL technique employs the bi-directional gated recurrent unit (BiGRU) technique for the classification and identification of zero-day attacks. Finally, the detection performance of the BiGRU technique is further enhanced through the implementation of the subtraction average-based optimizer (SABO)-based tuning process. The performance of the ZDAD-EODL approach is investigated on the benchmark dataset. The comparison study of the ZDAD-EODL approach portrayed a superior accuracy value of 98.47% over existing techniques.

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