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Updated: May 25, 2025

Electrowetting-based Digital Microfluidics Platform for Automated Enzyme-linked Immunosorbent Assay

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A Survey on ML Techniques for Multi-Platform Malware Detection: Securing PC, Mobile Devices, IoT, and Cloud

Jannatul Ferdous¹, Rafiqul Islam¹, Arash Mahboubi¹

¹School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, NSW 2640, Australia.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This study reviews machine learning for malware detection across PCs, mobile devices, IoT, and cloud platforms. It highlights the need for adaptable, cross-platform strategies against evolving cyber threats.

Keywords:

IoT malware PC malware cloud-based malware detection machine learning malware analysis malware detection mobile malware multi-platform malware

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Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

Malware poses a significant global threat, causing billions in financial losses.
Cybercriminals exploit evolving malware capabilities across diverse platforms like PCs, mobile, IoT, and cloud.
Existing research primarily focuses on single-platform malware detection, lacking a comprehensive cross-platform review.

Purpose of the Study:

To provide an extensive review of machine learning (ML) techniques for malware detection across multiple platforms.
To address the research gap in understanding and countering cross-platform malware threats.
To motivate future research in adaptable, cross-platform malware detection.

Main Methods:

Comprehensive literature review of ML-based malware detection since 2017.
Analysis of malware detection techniques for PC, mobile, IoT, and cloud environments.
Identification of current challenges and future research directions.

Main Results:

Identified a lack of holistic, cross-platform malware detection strategies in existing research.
Detailed the evolution of malware threats targeting diverse digital ecosystems.
Synthesized recent advancements in ML for malware detection across platforms.

Conclusions:

A platform-based understanding of malware detection is crucial for effective defense.
Developing adaptable, cross-platform ML techniques is essential for future cybersecurity.
This review provides a foundation for robust, evolving malware detection strategies.