# Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network

arXiv: https://arxiv.org/abs/2412.18932 · HTML: https://mlsanity.com/paper/2412.18932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [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)
- [Malware Detection with LSTM using Opcode Language](https://mlsanity.com/paper/1906.04593/) (2019)
- [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)
- [A Natural Language Processing Approach to Malware Classification](https://mlsanity.com/paper/2307.11032/) (2023)
- [Malware Classification with GMM-HMM Models](https://mlsanity.com/paper/2103.02753/) (2021)

## Abstract (arXiv)

The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of advanced machine learning techniques. In this research, we present a novel approach based on a hybrid architecture combining features extracted using a Hidden Markov Model (HMM), with a Convolutional Neural Network (CNN) then used for malware classification. Inspired by the strong results in previous work using an HMM-Random Forest model, we propose integrating HMMs, which serve to capture sequential patterns in opcode sequences, with CNNs, which are adept at extracting hierarchical features. We demonstrate the effectiveness of our approach on the popular Malicia dataset, and we obtain superior performance, as compared to other machine learning methods -- our results surpass the aforementioned HMM-Random Forest model. Our findings underscore the potential of hybrid HMM-CNN architectures in bolstering malware classification capabilities, offering several promising avenues for further research in the field of cybersecurity.