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

被引:2
|
作者
Almuqren, Latifah [1 ]
Aljameel, Sumayh S. [2 ]
Alqahtani, Hamed [3 ]
Alotaibi, Saud S. [4 ]
Hamza, Manar Ahmed [5 ]
Salama, Ahmed S. [6 ]
机构
[1] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[2] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, SAUDI ARAMCO Cybersecur Chair, Comp Sci Dept, POB 1982, Dammam 31441, Saudi Arabia
[3] King Khalid Univ, Coll Comp Sci, Dept Informat Syst, Unit Cybersecur, Abha 61421, Saudi Arabia
[4] Umm Al Qura Univ, Coll Comp & Informat Syst, Dept Informat Syst, Mecca 24382, Saudi Arabia
[5] Prince Sattam Bin Abdulaziz Univ, Dept Comp & Self Dev, Preparatory Year Deanship, Al Kharj 16278, Saudi Arabia
[6] Future Univ Egypt, Fac Engn & Technol, Dept Elect Engn, New Cairo 11845, Egypt
关键词
smart grids; DDoS attacks; cybersecurity; feature selection; deep autoencoder; smart cities; SERVICE ATTACK;
D O I
10.3390/s23177370
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.
引用
收藏
页数:15
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