# Classifying Malware Images with Convolutional Neural Network Models

arXiv: https://arxiv.org/abs/2010.16108 · HTML: https://mlsanity.com/paper/2010.16108/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

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

- [A Survey of Malware Detection Using Deep Learning](https://mlsanity.com/paper/2407.19153/) (2024)
- [CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls](https://mlsanity.com/paper/2405.02548/) (2024)
- [Deep Multi-Task Learning for Malware Image Classification](https://mlsanity.com/paper/2405.05906/) (2024)
- [New Approach to Malware Detection Using Optimized Convolutional Neural Network](https://mlsanity.com/paper/2301.11161/) (2023)
- [BinImg2Vec: Augmenting Malware Binary Image Classification with Data2Vec](https://mlsanity.com/paper/2209.00782/) (2022)

## Abstract (arXiv)

Due to increasing threats from malicious software (malware) in both number and complexity, researchers have developed approaches to automatic detection and classification of malware, instead of analyzing methods for malware files manually in a time-consuming effort. At the same time, malware authors have developed techniques to evade signature-based detection techniques used by antivirus companies. Most recently, deep learning is being used in malware classification to solve this issue. In this paper, we use several convolutional neural network (CNN) models for static malware classification. In particular, we use six deep learning models, three of which are past winners of the ImageNet Large-Scale Visual Recognition Challenge. The other three models are CNN-SVM, GRU-SVM and MLP-SVM, which enhance neural models with support vector machines (SVM). We perform experiments using the Malimg dataset, which has malware images that were converted from Portable Executable malware binaries. The dataset is divided into 25 malware families. Comparisons show that the Inception V3 model achieves a test accuracy of 99.24%, which is better than the accuracy of 98.52% achieved by the current state-of-the-art system called the M-CNN model.