Multi-objective Markov-enhanced adaptive whale optimization cybersecurity model for binary and multi-class malware cyberthreat classification

Saif Ali Abd Alradha Alsaidi; Riyadh Rahef Nuiaa Al Ogaili; Zaid Abdi Alkareem Alyasseri; Dhiah Al-Shammary; Ayman Ibaida; Adam Slowik

doi:10.1016/j.jnlest.2025.100334

Journal of Electronic Science and Technology ›› 2025, Vol. 23 ›› Issue (4) :100334 DOI: 10.1016/j.jnlest.2025.100334

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Multi-objective Markov-enhanced adaptive whale optimization cybersecurity model for binary and multi-class malware cyberthreat classification

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^a College of Computer Science and Information Technology, Wasit University, Al-Kut, 52001, Iraq

^b Information Technology Research and Development Center, University of Kufa, Najaf, 54001, Iraq

^c College of Engineering, University of Warith Al-Anbiyaa, Karbala, 56001, Iraq

^d College of Computer Science and Information Technology, University of Al-Qadisiyah, Al-Diwaniyah, 58001, Iraq

^e Intelligent Technology Innovation Lab, Victoria University, Melbourne, 3011, Australia

^f Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, 75-453, Poland

^* E-mail addresses: salsaidi@uowasit.edu.iq (S.A.A. Alradha Alsaidi),

riyadh@uowasit.edu.iq (R.R.N. Al Ogaili),

zaid.alyasseri@uokufa.edu.iq (Z.A.A. Alyasseri),

d.alshammary@qu.edu.iq (D. Al-Shammary),

ayman.ibaida@vu.edu.au (A. Ibaida),

adam.slowik@tu.koszalin.pl (A. Slowik).

Saif Ali Abd Alradha Alsaidi is an associate professor of computer science and currently serves as the Dean of the College of Computer Science & Information Technology at Wasit University (UOW), Al-Kut, Iraq. He earned the B.Sc. degree from Al-Mustansiriyah University, Baghdad, Iraq in 2007, followed by the M.Sc. degree in information systems from Osmania University (OU), Hyderabad, India (2012–2014), and completed the Ph.D. degree in computer science (artificial intelligence) at the University of Technology, Baghdad, Iraq in 2021. His key interests include artificial intelligence, machine learning, text mining, cloud computing, Internet of things (IoT), e-government, and e-governance.

Riyadh Rahef Nuiaa Al Ogaili received the B.Sc. degree in computer science from Baghdad College of Economic Sciences University, Baghdad, Iraq in 2004, the M.Sc. degree in information systems from OU in 2014, and the Ph.D. degree in advanced Internet security and monitoring (Internet infrastructure security/cybersecurity) from the Cybersecurity Research Centre (CYRES), Universiti Sains Malaysia (USM), Penang, Malaysia in 2023. He is currently an assistant professor at UOW. His research interests include cybersecurity; network and IoT security; intrusion detection systems (IDS); machine and deep learning for security and optimization.

Zaid Abdi Alkareem Alyasseri received the B.Sc. degree in computer science from Babylon University, Al-Hillah, Iraq in 2007, the M.Sc. and Ph.D. degrees from USM in 2013 and 2020, respectively. He is currently an associate professor at the University of Kufa, Najaf, Iraq. His research interests include optimization, pattern recognition, EEG, brain-computer interface, signal and image processing, machine learning, and deep learning.

Dhiah Al-Shammary has received the Ph.D. degree in computer science from Royal Melbourne Institute of Technology (RMIT), Melbourne, Australia in 2014. He was awarded as the Best Ph.D. student and top publication during his Ph.D. period. He has several years of experience in both education and industry. His main industrial experience came from Silicon Valley-based companies working on security projects, including non-traditional and quantum-scale encryption. He has worked at several universities in Australia and Iraq like RMIT and the University of Al-Qadisiyah. His research interests include performance modeling, Web services, compression, and encoding techniques, and distributed systems.

Ayman Ibaida received the Ph.D. degree from RMIT in 2014. He is currently a lecturer at Victoria University, Melbourne, Australia. His research interests include AI and machine learning in biomedical applications, diagnoses, and patient health records security as well as cybersecurity in healthcare systems.

Adam Slowik received the B.S. and M.S. degrees in computer engineering and electronics from Koszalin University of Technology (PK), Koszalin, Poland in 2001, the Ph.D. degree (Hons.) from PK in 2007, and the Dr. Habil. degree in computer science (intelligent systems) from Czestochowa University of Technology, Czestochowa, Poland in 2013. Since October 2013, he has been an associate professor at the Department of Electronics and Computer Science, PK. He is currently the Head of the Department of Computer Engineering, PK. His research interests include soft computing, computational intelligence, and particularly, bio-inspired optimization algorithms and their engineering applications. He is the author or coauthor of over 100 refereed articles in international journals, two books, and conference proceedings, including one invited talk.

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Abstract

The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger; the real threat lies in the obfuscation of these cyberattacks, as they constantly change their behavior, making detection more difficult. Numerous researchers and developers have devoted considerable attention to this topic; however, the research field has not yet been fully saturated with high-quality studies that address these problems. For this reason, this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization (MOMEAWO) cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach. The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing, detecting, and classifying the behavior of obfuscated malware within their respective families. The proposed model includes three classification types: Binary classification and multi-class classification (e.g., four families and 16 malware families). To evaluate the performance of this model, we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis (CIC-MalMem-2022) that contains balanced data. The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.

Keywords

Malware cybersecurity attacks / Malware detection and classification / Markov chain / Multi-objective / MOMEAWO cybersecurity model

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Saif Ali Abd Alradha Alsaidi, Riyadh Rahef Nuiaa Al Ogaili, Zaid Abdi Alkareem Alyasseri, Dhiah Al-Shammary, Ayman Ibaida, Adam Slowik. Multi-objective Markov-enhanced adaptive whale optimization cybersecurity model for binary and multi-class malware cyberthreat classification. Journal of Electronic Science and Technology, 2025, 23(4): 100334 DOI:10.1016/j.jnlest.2025.100334

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