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Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Hanan Abdullah Mengash1, Jaber S. Alzahrani2, Majdy M. Eltahir3, Fahd N. Al-Wesabi4, Abdullah Mohamed5, Manar Ahmed Hamza6,*, Radwa Marzouk7

1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
3 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
4 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5 Research Centre, Future University in Egypt, New Cairo, 11745, Egypt
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
7 Department of Mathematics, Faculty of Science, Cairo University, Giza, 12613, Egypt

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computer Systems Science and Engineering 2023, 45(2), 1393-1407. https://doi.org/10.32604/csse.2023.030328

Abstract

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.

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APA Style
Mengash, H.A., Alzahrani, J.S., Eltahir, M.M., Al-Wesabi, F.N., Mohamed, A. et al. (2023). Search and rescue optimization with machine learning enabled cybersecurity model. Computer Systems Science and Engineering, 45(2), 1393-1407. https://doi.org/10.32604/csse.2023.030328
Vancouver Style
Mengash HA, Alzahrani JS, Eltahir MM, Al-Wesabi FN, Mohamed A, Hamza MA, et al. Search and rescue optimization with machine learning enabled cybersecurity model. Comput Syst Sci Eng. 2023;45(2):1393-1407 https://doi.org/10.32604/csse.2023.030328
IEEE Style
H.A. Mengash et al., "Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model," Comput. Syst. Sci. Eng., vol. 45, no. 2, pp. 1393-1407. 2023. https://doi.org/10.32604/csse.2023.030328



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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