# Application of Tabular Transformer Architectures for Operating System Fingerprinting

arXiv: https://arxiv.org/abs/2502.09084 · HTML: https://mlsanity.com/paper/2502.09084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 68 references that are in the map.
- First posted on arXiv 13 Feb 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification](https://mlsanity.com/paper/2202.06335/) (2022)
- [NetGPT: Generative Pretrained Transformer for Network Traffic](https://mlsanity.com/paper/2304.09513/) (2023)
- [TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation](https://mlsanity.com/paper/2403.05822/) (2024)
- [A new hope for network model generalization](https://mlsanity.com/paper/2207.05843/) (2022)
- [Rethinking Data-driven Networking with Foundation Models: Challenges and Opportunities](https://mlsanity.com/paper/2211.06494/) (2022)
- [NetBench: A Large-Scale and Comprehensive Network Traffic Benchmark Dataset for Foundation Models](https://mlsanity.com/paper/2403.10319/) (2024)
- [Towards a graph-based foundation model for network traffic analysis](https://mlsanity.com/paper/2409.08111/) (2024)
- [NorBERT: NetwOrk Representations through BERT for Network Analysis and Management](https://mlsanity.com/paper/2206.10472/) (2022)

## Built on by (most cited first, 1 of 1)

- [Reliable Hierarchical Operating System Fingerprinting via Conformal Prediction](https://mlsanity.com/paper/2602.12825/) (2026)

## Abstract (arXiv)

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.