WFP models trained and tested on scripted browser traffic achieve near-perfect accuracy (NetCLR: 98.9%, TF: 98.3%, Var-CNN: 98.9%), but accuracy collapses to below 10% — with F1-scores near zero (TF: 0.034, Var-CNN: 0.022, ARES: 0.025) — when trained on scripted traffic and tested on real human traffic (CrossDomain). This structural mismatch means scripted datasets overestimate the real-world WFP threat by more than 80 percentage points.

Cross-user generalization (LeaveOneUser) causes 30–40% accuracy drops across all nine state-of-the-art WFP models in closed-world settings: Var-CNN falls from 75.6% to 46.8%, DF from 78.2% to 48.7%, and TikTok/NetCLR from over 80% to approximately 50% in the 10-site task. Models lose recall faster than precision, indicating overfitting to frequent behavioral patterns of seen users rather than site-invariant features.

§5.3–5.4 / Table 1 / Figure 6 evaluation

In the boundless (boundary-free, session-unsegmented) setting with Human+Claude training, TrafficSliver reduces WFNet accuracy from 0.760 to 0.592, Cluster Anonymization (Palette) to 0.647, and Zero-delay (FRONT) to 0.706 at 2000 training samples. All three defenses assume clean session or page-load boundaries and are less effective against an attacker classifying arbitrary 5,000-packet continuous segments.

§5.7 / Table 2 evaluation

Training WFP classifiers with LLM multi-agent simulated traces boosts cross-user accuracy to the 80% range (TikTok: 50.3%→84.9%, NetCLR: 53.6%→77.3%) compared to under 15% for models trained on scripted-only traffic. ClaudeOnly training outperforms ScriptedOnly baselines by approximately 3× on held-out human traces, using only 20% of the data volume, at a cost of roughly $10/GB versus $35/GB for human collection.

§5.4 / Figure 6 detection

In open-world evaluation against 40 unseen background websites, models trained with Human+Claude traces suffer only 2–3% accuracy degradation versus 5–8% for Human-only training. This demonstrates that LLM-augmented data provides behavioral regularization that reduces false positives on background traffic, sustaining closed-world accuracy under realistic adversarial conditions.

§5.5 / Figure 7 evaluation

Increasing Nym's per-hop mixing delay (λM) counterintuitively worsens WF resistance: without cover traffic, raising λM to 100 ms raises Tik-Tok F1 from 0.91 (no mixing) to 0.98. Larger delays create a visible temporal gap between outgoing request bursts and incoming response bursts, making the Transposition feature set leak 95.5% classifier accuracy versus 88.5% with no delay at all.

2026-joll-s-website-fingerprinting-nym §5.2, Table 4, Table 5 website-fingerprintml-classifiertraffic-shape

With infinite training time, Laserbeak achieves 93.5%, 95.9%, and 95.9% accuracy against ephemeral padding, FRONT, and Interspace respectively, compared to 96.5% undefended — confirming that padding-only defenses provide no meaningful protection against a sufficiently trained deep-learning WF adversary. Only ephemeral blocking defenses retain measurable protection, reducing Laserbeak to 71.8% accuracy under infinite training versus 96.5% undefended.

2026-pulls-ephemeral-network-layer-fingerprinting §5.3, Table 2 ml-classifierwebsite-fingerprinttraffic-shape

Joint multi-task training with a combined loss L_joint = L_site + λ·L_pers shows that increasing λ from 0 to 2 raises mixed-site persona accuracy from approximately 45% to approximately 80% while website accuracy declines only from approximately 90% to approximately 75%, demonstrating a wide regime where an attacker can gain strong persona inference at modest cost to existing WFP capability.

2026-song-personafingerprint-measuring-persona §4.4, §5.7.2, Figure 5 website-fingerprintml-classifiertraffic-shape

Using only 1,000-packet windows of signed packet lengths and inter-arrival times (no payload, no URLs, no cookies), a passive adversary achieves approximately 84% accuracy at inferring behavioral persona in a mixed-site open-world setting spanning 10 modern websites and 15 canonical personas plus an open-world class. Per-site persona macro-F1 typically ranges from about 0.78 to 0.91 across representative platforms including Bilibili, eBay, Yahoo, Zhihu, and LinkedIn.

2026-song-personafingerprint-measuring-persona §5.3, §5.5, Abstract website-fingerprinttraffic-shapeml-classifier

A plug-and-play Boundary Preserving Aggregation Module (overlapping window partitioning with joint packet- and burst-level features, W=20ms, stride=10ms) consistently improves existing WF baselines without architectural modification: applied to DF, AUC rises from 0.780 to 0.901 and P@5 from 0.315 to 0.545; applied to ARES'25, P@5 rises from 0.869 to 0.900 in the open-world 5-tab setting. The module's consistent gains across all three tested baselines confirm that fixed non-overlapping window segmentation is a structural vulnerability in prior WF pipelines.

2026-yuan-demux-boundary-aware-multi-scale §V-G, Figure 5 website-fingerprintml-classifiertraffic-shape

DEMUX achieves a P@5 of 0.943 and MAP@5 of 0.961 in the closed-world 5-tab multi-tab website fingerprinting setting, outperforming the strongest prior baseline (ARES'25) by 9.2 and 6.2 percentage points respectively. ARES'25's P@K degrades from 0.900 at 2-tab to 0.851 at 5-tab (a drop of 4.9 pp), while DEMUX improves from 0.926 to 0.943 over the same range, expanding the absolute margin from 2.6 to over 9 points.

2026-yuan-demux-boundary-aware-multi-scale §V-B, Table II website-fingerprintml-classifiertraffic-shape