A Survey on Machine Learning Techniques in Multi-platform Malware Detection: Securing PC, Mobile Devices, IoT, and Cloud Environments

Malware has emerged as a significant threat to end-users, businesses, and governments, resulting in financial losses of billions of dollars. Cybercriminals have found malware to be a lucrative business because of its evolving capabilities and ability to target diverse platforms such as PCs, mobile devices, IoT, and cloud platforms. While previous studies have explored single platform-based malware detection, no existing research has comprehensively reviewed malware detection across diverse platforms using machine learning (ML) tactics. With the rise of malware on PC/laptop devices, it is now targeting mobile devices and IoT systems, posing a significant threat to cloud environments. Therefore, a platform-based understanding of malware detection and defense mechanisms is essential for countering this evolving threat. To fill this gap and motivate further research, we present an extensive review of malware detection using ML techniques with respect to PCs, mobile devices, IoT, and cloud platforms. This paper begins with an overview of malware, including its definition, prominent types, impacts, analysis, and features. It presents a comprehensive review of machine learning-based malware detection from recent literature, including journal articles, conference proceedings, and online resources published since 2017. This survey also offers insights into current challenges and outlines future directions for developing adaptable cross-platform malware detection techniques. This survey is crucial for understanding the evolving threat landscape and developing robust detection strategies.