# Ransomware Detection and Classification using Machine Learning

arXiv: https://arxiv.org/abs/2311.16143 · HTML: https://mlsanity.com/paper/2311.16143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 5 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Reliable Detection of Compressed and Encrypted Data](https://mlsanity.com/paper/2103.17059/) (2021)
- [AI-based Malware and Ransomware Detection Models](https://mlsanity.com/paper/2207.02108/) (2022)
- [Ransomware Classification and Detection With Machine Learning Algorithms](https://mlsanity.com/paper/2207.00894/) (2022)
- [Towards resilient machine learning for ransomware detection](https://mlsanity.com/paper/1812.09400/) (2018)
- [Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis](https://mlsanity.com/paper/2210.11235/) (2022)
- [Ransomware Detection and Classification Strategies](https://mlsanity.com/paper/2304.04398/) (2023)

## Abstract (arXiv)

Vicious assaults, malware, and various ransomware pose a cybersecurity threat, causing considerable damage to computer structures, servers, and mobile and web apps across various industries and businesses. These safety concerns are important and must be addressed immediately. Ransomware detection and classification are critical for guaranteeing rapid reaction and prevention. This study uses the XGBoost classifier and Random Forest (RF) algorithms to detect and classify ransomware attacks. This approach involves analyzing the behaviour of ransomware and extracting relevant features that can help distinguish between different ransomware families. The models are evaluated on a dataset of ransomware attacks and demonstrate their effectiveness in accurately detecting and classifying ransomware. The results show that the XGBoost classifier, Random Forest Classifiers, can effectively detect and classify different ransomware attacks with high accuracy, thereby providing a valuable tool for enhancing cybersecurity.