# A Natural Language Processing Approach to Malware Classification

arXiv: https://arxiv.org/abs/2307.11032 · HTML: https://mlsanity.com/paper/2307.11032/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 55 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS](https://mlsanity.com/paper/1912.06059/) (2019)
- [A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection](https://mlsanity.com/paper/2203.09938/) (2022)
- [Transfer Learning for Image-Based Malware Classification](https://mlsanity.com/paper/1903.11551/) (2019)
- [Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features](https://mlsanity.com/paper/1908.05759/) (2019)
- [Malware Classification Using Long Short-Term Memory Models](https://mlsanity.com/paper/2103.02746/) (2021)
- [Malware Detection with LSTM using Opcode Language](https://mlsanity.com/paper/1906.04593/) (2019)
- [An Empirical Analysis of Image-Based Learning Techniques for Malware Classification](https://mlsanity.com/paper/2103.13827/) (2021)
- [Random Forest for Malware Classification](https://mlsanity.com/paper/1609.07770/) (2016)
- [Generative Adversarial Networks and Image-Based Malware Classification](https://mlsanity.com/paper/2207.00421/) (2022)
- [Malware Classification with GMM-HMM Models](https://mlsanity.com/paper/2103.02753/) (2021)
- [CNN vs ELM for Image-Based Malware Classification](https://mlsanity.com/paper/2103.13820/) (2021)

## Built on by (most cited first, 4 of 4)

- [LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs](https://mlsanity.com/paper/2402.18443/) (2024)
- [Generating Synthetic Malware Samples Using Generative AI](https://mlsanity.com/paper/2604.22084/) (2026)
- [Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network](https://mlsanity.com/paper/2412.18932/) (2024)
- [Robustness of Selected Learning Models under Label-Flipping Attack](https://mlsanity.com/paper/2501.12516/) (2025)

## Abstract (arXiv)

Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) networks. In this research, we consider a hybrid architecture, where HMMs are trained on opcode sequences, and the resulting hidden states of these trained HMMs are used as feature vectors in various classifiers. In this context, extracting the HMM hidden state sequences can be viewed as a form of feature engineering that is somewhat analogous to techniques that are commonly employed in Natural Language Processing (NLP). We find that this NLP-based approach outperforms other popular techniques on a challenging malware dataset, with an HMM-Random Forrest model yielding the best results.