Reliable Hierarchical Operating System Fingerprinting via Conformal Prediction

Operating System (OS) fingerprinting is critical for network security, but conventional methods do not provide formal uncertainty quantification mechanisms. Conformal Prediction (CP) could be directly wrapped around existing methods to obtain prediction sets with guaranteed coverage. However, a direct application of CP would treat OS identification as a flat classification problem, ignoring the natural taxonomic structure of OSs and providing brittle point predictions. This work addresses these limitations by introducing and evaluating two distinct structured CP strategies: level-wise CP (L-CP), which calibrates each hierarchy level independently, and projection-based CP (P-CP), which ensures structural consistency by projecting leaf-level sets upwards. Our results demonstrate that, while both methods satisfy validity guarantees, they expose a fundamental trade-off between level-wise efficiency and structural consistency. L-CP yields tighter prediction sets suitable for human forensic analysis but suffers from taxonomic inconsistencies. Conversely, P-CP guarantees hierarchically consistent, nested sets ideal for automated policy enforcement, albeit at the cost of reduced efficiency at coarser levels.

Application of Tabular Transformer Architectures for Operating System Fingerprinting

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.