Application of Tabular Transformer Architectures for Operating System Fingerprinting
Abstract
Operating System (OS) fingerprinting is essential for network management and cybersecurity, enabling accurate device identification based on network traffic analysis. Traditional rule-based tools such as Nmap and p0f face challenges in dynamic environments due to frequent OS updates and obfuscation techniques. While Machine Learning (ML) approaches have been explored, Deep Learning (DL) models, particularly Transformer architectures, remain unexploited in this domain. This study investigates the application of Tabular Transformer architectures-specifically TabTransformer and FT-Transformer-for OS fingerprinting, leveraging structured network data from three publicly available datasets. Our experiments demonstrate that FT-Transformer generally outperforms traditional ML models, previous approaches and TabTransformer across multiple classification levels (OS family, major, and minor versions). The results establish a strong foundation for DL-based OS fingerprinting, improving accuracy and adaptability in complex network environments. Furthermore, we ensure the reproducibility of our research by providing an open-source implementation.
Cite
@article{arxiv.2502.09084,
title = {Application of Tabular Transformer Architectures for Operating System Fingerprinting},
author = {Rubén Pérez-Jove and Cristian R. Munteanu and Alejandro Pazos and Jose Vázquez-Naya},
journal= {arXiv preprint arXiv:2502.09084},
year = {2025}
}
Comments
Submitted as a preprint (not peer reviewed). 22 pages, 9 figures. Code and datasets available at: https://github.com/rubenpjove/tabularT-OS-fingerprinting