# Ransomware Classification and Detection With Machine Learning Algorithms

arXiv: https://arxiv.org/abs/2207.00894 · HTML: https://mlsanity.com/paper/2207.00894/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2022.

## Built on by (most cited first, 5 of 5)

- [Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms](https://mlsanity.com/paper/2306.08060/) (2023)
- [An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey](https://mlsanity.com/paper/2402.17045/) (2024)
- [WannaLaugh: A Configurable Ransomware Emulator -- Learning to Mimic Malicious Storage Traces](https://mlsanity.com/paper/2403.07540/) (2024)
- [A Novel IoT-based Framework for Non-Invasive Human Hygiene Monitoring using Machine Learning Techniques](https://mlsanity.com/paper/2207.03529/) (2022)
- [Ransomware Detection and Classification using Machine Learning](https://mlsanity.com/paper/2311.16143/) (2023)

## Abstract (arXiv)

Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F-beta, and precision scores.