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Open Access

ARTICLE

On the Resilience of Traffic Features under Concept Drift in Hidden Service Fingerprinting

Xiaoyun Yuan1,2,3,*, Zhengge Yi1,2, Jingxi Zhang1,2, Hairui Zhang3
1 School of Cyberspace Security, Information Engineering University, Zhengzhou, China
2 Key Laboratory of Cyberspace Situation Awareness of Henan Province, Zhengzhou, China
3 Former Army Artillery and Air Defense Academy Zhengzhou Campus, Zhengzhou, China
* Corresponding Author: Xiaoyun Yuan. Email: email
(This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.072275

Received 23 August 2025; Accepted 06 January 2026; Published online 12 June 2026

Abstract

Website Fingerprinting (WF) has emerged as a promising technique for identifying user access patterns to Hidden Services (HS). Despite growing interest in WF for HS, the absence of well-established foundations and systematic guidelines for feature selection undercuts the robustness of WF techniques in the face of concept drift. To address this gap, we present an empirical study focusing on feature resilience under concept drift in WF for HS. Specifically, we categorize features into network-specific and network-agnostic groups and quantify their information leakage potential via mutual information. We further assess each feature’s resilience to concept drift by evaluating its distributional stability through Kullback-Leibler (KL) divergence. To support our analysis, we construct a dedicated dataset capturing HS traffic across multiple time windows. Our evaluation reveals that although often overlooked by existing studies, network-specific features—especially time-based ones—not only leak more identifiable information but also exhibit greater stability against distributional shifts over time. We further investigate the underlying root causes of this phenomenon, offering insights that expand the theoretical underpinnings of WF and guide future feature selection strategies.

Keywords

Website fingerprinting; concept drift; Tor; feature analysis; information leakage; hidden services

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