TY  - EJOU
AU  - Almofarreh, Mona 
AU  - Alshahrani, Amnah 
AU  - Alharbi, Nouf Helal 
AU  - Ahmed,  Omer 
AU  - Alshahrani, Hussain 
AU  - Alzahrani, Abdulrahman 
AU  - Alshahrani, Mohammed Mujib 
AU  - Alhashmi, Asma A. 

TI  - Boosting Cybersecurity: A Zero-Day Attack Detection Approach Using Equilibrium Optimiser with Deep Learning Model
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - Zero-day attack; cybersecurity; deep learning; intrusion detection systems equilibrium optimiser

DO  - 10.32604/cmes.2025.070545