Malware Detection and AI Integration: A Systematic Review of Current Trends and Future Directions

M. Mohsin Raza^1,#, Muhammad Umair^1,#, Imran Arshad Choudhry¹, Muhammad Qasim¹, Muhammad Tahir Naseem^2,*, Mamoona Naveed Asghar³, Daniel Gavilanes^4,5,6,7, Manuel Masias Vergara^4,8,9, Imran Ashraf^10,*
1 Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, 54782, Punjab, Pakistan
2 Department of Electronic Engineering, Yeungnam University, Gyeongsan-si, 38541, Republic of Korea
3 School of Computer Science, University of Galway, Galway, H91 TK33, Ireland
4 Higher Polytechnic School, Universidad Europea del Atlantico, Isabel Torres 21, Santander, 39011, Spain
5 Engineering Research and Innovation Group, Universidad Internacional Iberoamericana, Campeche, 24560, Mexico
6 Department of Projects, Universidade Internacional do Cuanza, Cuito, EN250, Bie, Angola
7 Research Group on Foods, Nutritional Biochemistry and Health, Fundacion Universitaria Internacional de Colombia, Bogota, 11131, Colombia
8 Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
9 Universidad de La Romana, La Romana, 22000, Republica Dominicana
10 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, 38541, Republic of Korea
* Corresponding Author: Muhammad Tahir Naseem. Email: email ; Imran Ashraf. Email: email
# These authors contributed equally to this work

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.074164

Received 04 October 2025; Accepted 10 December 2025; Published online 28 January 2026

Abstract

Over the past decade, the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks. Traditional detection techniques, while still in use, often fall short when confronted with modern threats that use advanced evasion strategies. This systematic review critically examines recent developments in malware detection, with a particular emphasis on the role of artificial intelligence (AI) and machine learning (ML) in enhancing detection capabilities. Drawing on literature published between 2019 and 2025, this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore, SpringerLink, ScienceDirect, and ACM Digital Library. In doing so, it explores the evolution of malware, evaluates detection methods, assesses the quality and limitations of widely used datasets, and identifies key challenges facing the field. Unlike existing surveys, this work offers a structured comparison of AI-driven frameworks and provides a detailed account of emerging techniques such as hybrid detection frameworks and image-based analysis. The findings indicate that AI-based models trained on diverse, high-quality datasets consistently outperform conventional methods, particularly when supported by feature engineering, explainable AI and a multi-faceted strategy. The review concludes by outlining future research directions, including the need for standardized datasets, enhanced adversarial robustness, and the integration of privacy-preserving mechanisms in malware detection systems.