mlsanityData to 7 Oct 2026

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

arXiv 2410.13894 ↗First posted 14 Oct 2024cs.CR cs.AIcited by 1

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Abstract (arXiv)

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).

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 158 references that are in the map. First posted on arXiv 14 Oct 2024.

Builds on

15 of 158 references.

References · 15

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  11. 2021An Automated and Comprehensive Framework for IoT Botnet Detection and Analysis (IoT-BDA)cited by 3
  12. 2023A Survey on Cross-Architectural IoT Malware Threat Huntingcited by 2
3 more
  1. 2018Evolving SDN for Low-Power IoT Networkscited by 1
  2. 2020Tools and Techniques for Malware Detection and Analysiscited by 1
  3. 2021Recommending API Function Calls and Code Snippets to Support Software Developmentcited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learningcited by 0