JACN 2025 Vol.13(2): 37-50
DOI: 10.18178/jacn.2025.13.2.297
Malicious Malware Identification in Mac OS X using Supervised Learning Techniques
Imran Khan1, Fazal Wahab2,*, and Sarath Bodapathi3
1. Department of Informatics, University of Sussex, UK
2. National University of Computer and Emerging Sciences, Faisalabad, Pakistan
3. Department of Engineering and Computer Science, University of Hertfordshire, UK
Email: imran.khan@sussex.ac.uk (I.K.), studentpk2024@gmail.com (F.W.), sarath.bodapathi@herts.ac.uk (S.B.)
*Corresponding author
Manuscript received August 6, 2025; accepted October 11, 2025; published December 25, 2025
Abstract—The proliferation of malicious software is rapidly escalating, posing significant challenges in the identification and classification of emerging threats. The inefficiency of conventional approaches employed in malware detection has led to the exploration of Machine Learning (ML) algorithms as a promising solution. This study attempts to enhance existing cybersecurity techniques and improve system protection against potential threats. The objective is to create an intelligent malware detection system specifically designed for accurately and consistently identifying hazardous malware in the Macintosh Operating System X (Mac OS X). We determine the optimal supervised model’s hyperparameter value to improve Mac OS X’s security. We evaluate the efficacy of these supervised algorithms in comparison to boosting approaches. The experimental findings demonstrate that both the histogram-based boosting approaches and the random forest classifier have exhibited remarkable efficacy in identifying malware on Mac OS X. The algorithms achieved a validation data accuracy of 94% and 91%, and a testing data accuracy of 86% and 89%, respectively. The results are also compared with other state-of-the-art methods. These results are crucial for Mac OS X users, as they ensure the protection of their systems and data against potential threats. Additionally, this approach can also be implemented with different systems for security purposes.
Keywords—intelligent system, malware identification, artificial intelligence, supervised learning, machine learning
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Cite: Imran Khan, Fazal Wahab, and Sarath Bodapathi, "Malicious Malware Identification in Mac OS X using Supervised Learning Techniques," Journal of Advances in Computer Networks, vol. 13, no. 2, pp. 37-50, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (
CC BY 4.0).
