# Malware Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models

arXiv: https://arxiv.org/abs/2503.02144 · HTML: https://mlsanity.com/paper/2503.02144/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data](https://mlsanity.com/paper/2303.06514/) (2023)

## Abstract (arXiv)

This study investigates the performance of various classification models for a malware classification task using different feature sets and data configurations. Six models-Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, Random Forest (RF), and Extreme Gradient Boosting (XGB)-were evaluated alongside two deep learning models, Recurrent Neural Networks (RNN) and Transformers, as well as the Gemini zero-shot and few-shot learning methods. Four feature sets were tested including All Features, Literature Review Features, the Top 45 Features from RF, and Down-Sampled with Top 45 Features. XGB achieved the highest accuracy of 87.42% using the Top 45 Features, outperforming all other models. RF followed closely with 87.23% accuracy on the same feature set. In contrast, deep learning models underperformed, with RNN achieving 66.71% accuracy and Transformers reaching 71.59%. Down-sampling reduced performance across all models, with XGB dropping to 81.31%. Gemini zero-shot and few-shot learning approaches showed the lowest performance, with accuracies of 40.65% and 48.65%, respectively. The results highlight the importance of feature selection in improving model performance while reducing computational complexity. Traditional models like XGB and RF demonstrated superior performance, while deep learning and few-shot methods struggled to match their accuracy. This study underscores the effectiveness of traditional machine learning models for structured datasets and provides a foundation for future research into hybrid approaches and larger datasets.