{"ID":2876168,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00706","arxiv_id":"2509.00706","title":"X-PRINT:Platform-Agnostic and Scalable Fine-Grained Encrypted Traffic Fingerprinting","abstract":"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.","short_abstract":"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 restri...","url_abs":"https://arxiv.org/abs/2509.00706","url_pdf":"https://arxiv.org/pdf/2509.00706v1","authors":"[\"YuKun Zhu\",\"ManYuan Hua\",\"Hai Huang\",\"YongZhao Zhang\",\"Jie Yang\",\"FengHua Xu\",\"RuiDong Chen\",\"XiaoSong Zhang\",\"JiGuo Yu\",\"Yong Ma\"]","published":"2025-08-31T05:42:17Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[]","has_code":false}