# A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware

arXiv: https://arxiv.org/abs/2111.02388 · HTML: https://mlsanity.com/paper/2111.02388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability](https://mlsanity.com/paper/2107.11100/) (2021)
- [A Convolutional Transformation Network for Malware Classification](https://mlsanity.com/paper/1909.07227/) (2019)
- [Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference](https://mlsanity.com/paper/2105.05445/) (2021)
- [A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild](https://mlsanity.com/paper/2010.11026/) (2020)

## Abstract (arXiv)

This work explores the use of machine learning techniques on an Internet-of-Things firmware dataset to detect malicious attempts to infect edge devices or subsequently corrupt an entire network. Firmware updates are uncommon in IoT devices; hence, they abound with vulnerabilities. Attacks against such devices can go unnoticed, and users can become a weak point in security. Malware can cause DDoS attacks and even spy on sensitive areas like peoples' homes. To help mitigate this threat, this paper employs a number of machine learning algorithms to classify IoT firmware and the best performing models are reported. In a general comparison, the top three algorithms are Gradient Boosting, Logistic Regression, and Random Forest classifiers. Deep learning approaches including Convolutional and Fully Connected Neural Networks with both experimental and proven successful architectures are also explored.