# AI-based Malware and Ransomware Detection Models

arXiv: https://arxiv.org/abs/2207.02108 · HTML: https://mlsanity.com/paper/2207.02108/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [A Survey of Machine Learning Methods and Challenges for Windows Malware Classification](https://mlsanity.com/paper/2006.09271/) (2020)
- [Survey of Machine Learning Techniques for Malware Analysis](https://mlsanity.com/paper/1710.08189/) (2017)
- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions](https://mlsanity.com/paper/2102.06249/) (2021)
- [Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability](https://mlsanity.com/paper/2107.11100/) (2021)
- [Cyber Security in the Age of COVID-19: A Timeline and Analysis of Cyber-Crime and Cyber-Attacks during the Pandemic](https://mlsanity.com/paper/2006.11929/) (2020)

## Built on by (most cited first, 7 of 7)

- [Quantum Machine Learning for Malware Classification](https://mlsanity.com/paper/2305.09674/) (2023)
- [Semantic Preprocessing for LLM-based Malware Analysis](https://mlsanity.com/paper/2506.12113/) (2025)
- [Towards an in-depth detection of malware using distributed QCNN](https://mlsanity.com/paper/2312.12161/) (2023)
- [Use of Multi-CNNs for Section Analysis in Static Malware Detection](https://mlsanity.com/paper/2402.04102/) (2024)
- [Ransomware Detection and Classification using Machine Learning](https://mlsanity.com/paper/2311.16143/) (2023)
- [A Lean Transformer Model for Dynamic Malware Analysis and Detection](https://mlsanity.com/paper/2408.02313/) (2024)
- [ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification](https://mlsanity.com/paper/2607.03653/) (2026)

## Abstract (arXiv)

Cybercrime is one of the major digital threats of this century. In particular, ransomware attacks have significantly increased, resulting in global damage costs of tens of billion dollars. In this paper, we train and test different Machine Learning and Deep Learning models for malware detection, malware classification and ransomware detection. We introduce a novel and flexible solution that combines two optimized models for malware and ransomware detection. Our results demonstrate some improvements both in terms of detection performances and flexibility. In particular, our combined models pave the way for easier future enhancements using specialized and thus interchangeable detection modules.