超越分类准确率：量化加密暗网服务中的指纹复杂度
Beyond Classification Accuracy: Quantifying Fingerprint Complexity in Encrypted Darknet Services
- Charles Sturt University（查尔斯斯特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出指纹复杂度评分（FCS）框架，通过行为重叠等指标量化暗网服务指纹的内在复杂度，并在25个服务上验证了复杂度与识别性能的强负相关，为分析匿名网络行为信息泄露提供新视角。
AI中文摘要：
现有的暗网流量研究主要通过分类性能来评估服务的指纹可识别性，但对于为何某些服务更容易或更难被识别，提供的洞察有限。本文引入了指纹复杂度评分（FCS），这是一个利用行为重叠、不确定性、分歧和持续混淆来量化暗网服务指纹内在复杂度的框架。在Tor、I2P、FreeNet和ZeroNet匿名网络上的25个服务上进行的实验揭示了指纹复杂度的显著差异，其中行为重叠成为主要贡献因素。使用随机森林、极端随机树和XGBoost进行的验证表明，指纹复杂度与识别性能之间存在强烈的负相关关系（皮尔逊相关系数r = -0.706，斯皮尔曼相关系数ρ = -0.765，p < 0.001）。研究结果表明，服务的指纹可识别性从根本上受行为复杂度支配，为分析匿名网络中的行为信息泄露提供了新的视角。
英文摘要：
Existing darknet traffic studies primarily evaluate service fingerprintability through classification performance, providing limited insight into why certain services are easier or harder to identify. This paper introduces the Fingerprint Complexity Score (FCS), a framework for quantifying the intrinsic complexity of darknet service fingerprints using behavioral overlap, uncertainty, disagreement, and persistent confusion. Experiments on 25 services across the Tor, I2P, FreeNet, and ZeroNet anonymity networks reveal substantial variation in fingerprint complexity, with behavioral overlap emerging as the dominant contributor. Validation using Random Forest, Extra Trees, and XGBoost demonstrates a strong inverse relationship between fingerprint complexity and recognition performance (Pearson r = -0.706, Spearman \r{ho} = -0.765, p < 0.001). The findings show that service fingerprintability is fundamentally governed by behavioral complexity, providing a new perspective for analyzing behavioral information leakage in anonymity networks.
