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Intelligent Cybersecurity Classification Using Chaos Game Optimization with Deep Learning Model

Eatedal Alabdulkreem1, Saud S. Alotaibi2, Mohammad Alamgeer3,4, Radwa Marzouk5, Anwer Mustafa Hilal6,*, Abdelwahed Motwakel6, Abu Sarwar Zamani6, Mohammed Rizwanullah6
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
2 Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
4 Department of Computer Science and Bioinformatics, Singhania University, Pacheri Bari, Jhnujhunu, Rajasthan, India
5Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
* Corresponding Author: Anwer Mustafa Hilal. Email:

Computer Systems Science and Engineering 2023, 45(1), 971-983. https://doi.org/10.32604/csse.2023.030362

Received 24 March 2022; Accepted 17 May 2022; Issue published 16 August 2022

Abstract

Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent years. Both Machine Learning (ML) and Deep Learning (DL) classification models are useful in effective identification and classification of cyberattacks. In addition, the involvement of hyper parameters in DL models has a significantly influence upon the overall performance of the classification models. In this background, the current study develops Intelligent Cybersecurity Classification using Chaos Game Optimization with Deep Learning (ICC-CGODL) Model. The goal of the proposed ICC-CGODL model is to recognize and categorize different kinds of attacks made upon data. Besides, ICC-CGODL model primarily performs min-max normalization process to normalize the data into uniform format. In addition, Bidirectional Gated Recurrent Unit (BiGRU) model is utilized for detection and classification of cyberattacks. Moreover, CGO algorithm is also exploited to adjust the hyper parameters involved in BiGRU model which is the novelty of current work. A wide-range of simulation analysis was conducted on benchmark dataset and the results obtained confirmed the significant performance of ICC-CGODL technique than the recent approaches.

Keywords

Deep learning; chaos game optimization; cybersecurity; chaos game optimization; cyberattack

Cite This Article

E. Alabdulkreem, S. S. Alotaibi, M. Alamgeer, R. Marzouk, A. M. Hilal et al., "Intelligent cybersecurity classification using chaos game optimization with deep learning model," Computer Systems Science and Engineering, vol. 45, no.1, pp. 971–983, 2023.



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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