X-PRINT:Platform-Agnostic and Scalable Fine-Grained Encrypted Traffic Fingerprinting

Although encryption protocols such as TLS are widely de-ployed,side-channel metadata in encrypted traffic still reveals patterns that allow application and behavior inference.How-ever,existing fine-grained fingerprinting approaches face two key limitations:(i)reliance on platform-dependent charac-teristics,which restricts generalization across heterogeneous platforms,and(ii)poor scalability for fine-grained behavior identification in open-world settings. In this paper,we present X-PRINT,the first server-centric,URI-based framework for cross-platform fine-grained encrypted-traffic fingerprinting.X-PRINT systematically demonstrates that backend URI invocation patterns can serve as platform-agnostic invariants and are effective for mod-eling fine-grained behaviors.To achieve robust identifica-tion,X-PRINT further leverages temporally structured URI maps for behavior inference and emphasizes the exclusion of platform-or application-specific private URIs to handle unseen cases,thereby improving reliability in open-world and cross-platform settings.Extensive experiments across diverse cross-platform and open-world settings show that X-PRINT achieves state-of-the-art accuracy in fine-grained fingerprint-ing and exhibits strong scalability and robustness.