Towards Quantum Machine Learning in Ransomware Detection

Francesco Mercaldo, Francesco Mercaldo, Giovanni Ciaramella, Giovanni Ciaramella, Fabio Martinelli, Antonella Santone

2025

Abstract

Ransomware represent one of the most aggressive malware, due to their capability to prevent access to data and, as a consequence, totally paralyze the activity of any organization, such as companies, but also hospitals or banks. Considering the inadequacy of the signature-based approach, mainly exploited by free and commercial current antimalware, researchers are proposing new ransomware detection techniques based on deep learning. Recently, with the introduction of quantum computing, there is the possibility to introduce quantum principles into machine learning. In this paper, we propose an approach for ransomware detection through a quantum machine learning model aimed to analyse images obtained from the application opcodes. In particular, a hybrid model is proposed, composed of quantum and convolutional layers to discern between ransomware, generic malware, and trusted applications. To demonstrate that quantum machine learning is promising in ransomware detection, a real-world dataset composed by 15,000 applications is evaluated, by showing that the proposed hybrid quantum model obtains promising performances if compared to (fully) convolutional models (i.e., Alex Net, MobileNet, and a convolutional model developed by authors).

Paper Citation

in Harvard Style

Mercaldo F., Ciaramella G., Martinelli F. and Santone A. (2025). Towards Quantum Machine Learning in Ransomware Detection. In Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-760-3, SciTePress, pages 301-308. DOI: 10.5220/0013326400003979

in Bibtex Style

@conference{secrypt25,
author={Francesco Mercaldo and Giovanni Ciaramella and Fabio Martinelli and Antonella Santone},
title={Towards Quantum Machine Learning in Ransomware Detection},
booktitle={Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2025},
pages={301-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013326400003979},
isbn={978-989-758-760-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Towards Quantum Machine Learning in Ransomware Detection
SN - 978-989-758-760-3
AU - Mercaldo F.
AU - Ciaramella G.
AU - Martinelli F.
AU - Santone A.
PY - 2025
SP - 301
EP - 308
DO - 10.5220/0013326400003979
PB - SciTePress