# Towards an in-depth detection of malware using distributed QCNN

arXiv: https://arxiv.org/abs/2312.12161 · HTML: https://mlsanity.com/paper/2312.12161/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Absence of Barren Plateaus in Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2011.02966/) (2020)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [Theory of overparametrization in quantum neural networks](https://mlsanity.com/paper/2109.11676/) (2021)
- [A Survey of Machine Learning Methods and Challenges for Windows Malware Classification](https://mlsanity.com/paper/2006.09271/) (2020)
- [Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning](https://mlsanity.com/paper/2203.15368/) (2022)
- [Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability](https://mlsanity.com/paper/2107.11100/) (2021)
- [AI-based Malware and Ransomware Detection Models](https://mlsanity.com/paper/2207.02108/) (2022)
- [Quantum Machine Learning for Malware Classification](https://mlsanity.com/paper/2305.09674/) (2023)

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

- [Towards Quantum Machine Learning for Malicious Code Analysis](https://mlsanity.com/paper/2508.19381/) (2025)

## Abstract (arXiv)

Malware detection is an important topic of current cybersecurity, and Machine Learning appears to be one of the main considered solutions even if certain problems to generalize to new malware remain. In the aim of exploring the potential of quantum machine learning on this domain, our previous work showed that quantum neural networks do not perform well on image-based malware detection when using a few qubits. In order to enhance the performances of our quantum algorithms for malware detection using images, without increasing the resources needed in terms of qubits, we implement a new preprocessing of our dataset using Grayscale method, and we couple it with a model composed of five distributed quantum convolutional networks and a scoring function. We get an increase of around 20 % of our results, both on the accuracy of the test and its F1-score.