Reliable Hierarchical Operating System Fingerprinting via Conformal Prediction
Rub\'en P\'erez-Jove, Osvaldo Simeone, Alejandro Pazos, Jose V\'azquez-Naya

TL;DR
This paper introduces two structured conformal prediction strategies for OS fingerprinting that provide formal uncertainty quantification while respecting the hierarchical taxonomy of operating systems.
Contribution
It proposes level-wise and projection-based conformal prediction methods that incorporate OS hierarchy, addressing limitations of flat classification approaches.
Findings
Both methods satisfy validity guarantees.
Trade-off between efficiency and structural consistency.
Projection-based CP ensures hierarchical consistency.
Abstract
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…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Digital Media Forensic Detection · Advanced Graph Neural Networks
