# Comparative analysis of Transformer and Computer Vision-Based Models for Windows Malware Detection

> Malware authors aim to target Windows Portable Executable (PE) files because these files are the common format for software applications. A Static Malware Detection Technique Using the PE File characteristics and features. A comparative analysis is done using grayscaled image converting of PE header...

## Metadata
- Authors: Ashwath P, Premanth P, Shymala Gowri S
- Journal: IEEE Silchar Subsection Conference (SILCON)
- Published: 2025-11-06
- DOI: https://doi.org/10.1109/silcon67893.2025.11327108
- Citations: 1
- Source: OpenAlex

## Technology Hub
- Hub: Computer Vision
- Discipline: Computer Science / AI
- Hub URL: https://science-database.com/technology/computer-vision
- Hub llms.txt: https://science-database.com/technology/computer-vision/llms.txt

## Abstract
Malware authors aim to target Windows Portable Executable (PE) files because these files are the common format for software applications. A Static Malware Detection Technique Using the PE File characteristics and features. A comparative analysis is done using grayscaled image converting of PE headers by ResNet and TabTransformer models against Directly using the extracted PE features in their original tabular format. The direct feature-based TabTransformer approach is found to be better than image-based methods as shown by experiments on benchmark dataset such as the Ransomware PE Header dataset and the Windows Malware Detection dataset. The findings reveal how they can help enhance static malware detection by better capturing the complex interactions of the PE file features. In addition, no transformation of the data into an image is needed when using TabTransformer.

## Links
- DOI: https://doi.org/10.1109/silcon67893.2025.11327108
- OpenAlex: https://openalex.org/W7125008797
- JSON API: https://science-database.com/api/v1/technology/computer-vision

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Paper ID: oa-W7125008797 | Hub: computer-vision