Modelling of Metaheuristics with Machine Learning-Enabled Cybersecurity in Unmanned Aerial Vehicles

Mohammed Rizwanullah (), Hanan Abdullah Mengash, Mohammad Alamgeer, Khaled Tarmissi, Amira Sayed A. Aziz, Amgad Atta Abdelmageed, Mohamed Ibrahim Alsaid and Mohamed I. Eldesouki
Additional contact information
Mohammed Rizwanullah: Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
Hanan Abdullah Mengash: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mohammad Alamgeer: Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha, Saudi Arabia
Khaled Tarmissi: Department of Computer Science, College of Computing and Information System, Umm Al-Qura University, Mecca, Saudi Arabia
Amira Sayed A. Aziz: Department of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt
Amgad Atta Abdelmageed: Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
Mohamed Ibrahim Alsaid: Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
Mohamed I. Eldesouki: Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

Sustainability, 2022, vol. 14, issue 24, 1-15

Abstract: The adoption and recent development of Unmanned Aerial Vehicles (UAVs) are because of their widespread applications in the private and public sectors, from logistics to environment monitoring. The incorporation of 5G technologies, satellites, and UAVs has provoked telecommunication networks to advance to provide more stable and high-quality services to remote areas. However, UAVs are vulnerable to cyberattacks because of the rapidly expanding volume and poor inbuilt security. Cyber security and the detection of cyber threats might considerably benefit from the development of artificial intelligence. A machine learning algorithm can be trained to search for attacks that may be similar to other types of attacks. This study proposes a new approach: metaheuristics with machine learning-enabled cybersecurity in unmanned aerial vehicles (MMLCS-UAVs). The presented MMLCS-UAV technique mainly focuses on the recognition and classification of intrusions in the UAV network. To obtain this, the presented MMLCS-UAV technique designed a quantum invasive weed optimization-based feature selection (QIWO-FS) method to select the optimal feature subsets. For intrusion detection, the MMLCS-UAV technique applied a weighted regularized extreme learning machine (WRELM) algorithm with swallow swarm optimization (SSO) as a parameter tuning model. The experimental validation of the MMLCS-UAV method was tested using benchmark datasets. This widespread comparison study reports the superiority of the MMLCS-UAV technique over other existing approaches.

Keywords: metaheuristics; machine learning; cybersecurity; intrusion detection system; unmanned aerial vehicles (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/24/16741/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/24/16741/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:24:p:16741-:d:1002677

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().