Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges

TL;DR

This paper reviews how deep learning techniques are transforming IoT malware detection across various IoT domains, highlighting recent advances, challenges, and future research directions.

Contribution

It provides the first comprehensive survey on deep learning-based IoT malware analysis, covering multiple IoT categories and discussing research challenges and opportunities.

Findings

Deep learning achieves high detection accuracy for IoT malware.

Survey covers IoT domains like IIoT, IoMT, IoV, IoBT.

Identifies key research challenges in IoT malware detection.

Abstract

The Internet of Things (IoT) is one of the fastest-growing computing industries. By the end of 2027, more than 29 billion devices are expected to be connected. These smart devices can communicate with each other with and without human intervention. This rapid growth has led to the emergence of new types of malware. However, traditional malware detection methods, such as signature-based and heuristic-based techniques, are becoming increasingly ineffective against these new types of malware. Therefore, it has become indispensable to find practical solutions for detecting IoT malware. Machine Learning (ML) and Deep Learning (DL) approaches have proven effective in dealing with these new IoT malware variants, exhibiting high detection rates. In this paper, we bridge the gap in research between the IoT malware analysis and the wide adoption of deep learning in tackling the problems in this domain. As such, we provide a comprehensive review on deep learning based malware analysis across various categories of the IoT domain (i.e. Extended Internet of Things (XIoT)), including Industrial IoT (IIoT), Internet of Medical Things (IoMT), Internet of Vehicles (IoV), and Internet of Battlefield Things (IoBT).