# A Survey on Cross-Architectural IoT Malware Threat Hunting

arXiv: https://arxiv.org/abs/2306.07989 · HTML: https://mlsanity.com/paper/2306.07989/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 104 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection](https://mlsanity.com/paper/2012.07634/) (2020)
- [Survey of Machine Learning Techniques for Malware Analysis](https://mlsanity.com/paper/1710.08189/) (2017)
- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [Virus-MNIST: A Benchmark Malware Dataset](https://mlsanity.com/paper/2103.00602/) (2021)
- [AST-Based Deep Learning for Detecting Malicious PowerShell](https://mlsanity.com/paper/1810.09230/) (2018)
- [Learning from Context: Exploiting and Interpreting File Path Information for Better Malware Detection](https://mlsanity.com/paper/1905.06987/) (2019)
- [Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms](https://mlsanity.com/paper/2101.01015/) (2021)

## Built on by (most cited first, 2 of 2)

- [On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities](https://mlsanity.com/paper/2306.07118/) (2023)
- [Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges](https://mlsanity.com/paper/2410.13894/) (2024)

## Abstract (arXiv)

In recent years, the increase in non-Windows malware threats had turned the focus of the cybersecurity community. Research works on hunting Windows PE-based malwares are maturing, whereas the developments on Linux malware threat hunting are relatively scarce. With the advent of the Internet of Things (IoT) era, smart devices that are getting integrated into human life have become a hackers highway for their malicious activities. The IoT devices employ various Unix-based architectures that follow ELF (Executable and Linkable Format) as their standard binary file specification. This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches. Aided by a modern taxonomy, we discuss the feature representations, feature extraction techniques, and machine learning models employed in the surveyed works. We further provide more insights on the practical challenges involved in cross-architectural IoT malware threat hunting and discuss various avenues to instill potential future research.