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

arXiv: https://arxiv.org/abs/2410.13894 · HTML: https://mlsanity.com/paper/2410.13894/

## Key facts

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

## Builds on (most cited first, 15 of 15)

- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Lightweight Deep Learning for Resource-Constrained Environments: A Survey](https://mlsanity.com/paper/2404.07236/) (2024)
- [A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU](https://mlsanity.com/paper/2305.17473/) (2023)
- [Early Stage Malware Prediction Using Recurrent Neural Networks](https://mlsanity.com/paper/1708.03513/) (2017)
- [Cybersecurity for Industrial Control Systems: A Survey](https://mlsanity.com/paper/2002.04124/) (2020)
- [Robust Android Malware Detection System against Adversarial Attacks using Q-Learning](https://mlsanity.com/paper/2101.12031/) (2021)
- [IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN](https://mlsanity.com/paper/2202.04121/) (2022)
- [Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS](https://mlsanity.com/paper/2002.06383/) (2020)
- [Automated Machine Learning for Deep Learning based Malware Detection](https://mlsanity.com/paper/2303.01679/) (2023)
- [An Automated and Comprehensive Framework for IoT Botnet Detection and Analysis (IoT-BDA)](https://mlsanity.com/paper/2105.11061/) (2021)
- [A Survey on Cross-Architectural IoT Malware Threat Hunting](https://mlsanity.com/paper/2306.07989/) (2023)
- [Evolving SDN for Low-Power IoT Networks](https://mlsanity.com/paper/1809.07296/) (2018)
- [Tools and Techniques for Malware Detection and Analysis](https://mlsanity.com/paper/2002.06819/) (2020)
- [Recommending API Function Calls and Code Snippets to Support Software Development](https://mlsanity.com/paper/2102.07508/) (2021)

## Built on by (most cited first, 1 of 1)

- [FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning](https://mlsanity.com/paper/2507.07258/) (2025)

## 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).