DoHFuse's dual-branch architecture outperforms all five baselines (KNN, Random Forest, FCNN+GRU, LSTM, Transformer) across 100-, 200-, and 449-class closed-world settings with standard deviations under 0.5 percentage points, while the vanilla Transformer's standard deviation exceeds 5 percentage points at 449 classes (mean 61.50%). This demonstrates that attention-based models without recurrence-scale adaptations are unsuitable for the burst–gap temporal dynamics of DoH/3, and that multi-scale gating (DMAG-LSTM) is the key architectural driver of robustness.

Under DoH/3, EDNS(0) padding and HTTP/3 framing collapse packet-length diversity: more than 99% of request packets fall within 233–350 bytes and more than 99% of response packets fall within 555–777 bytes. Direction-signed length-sequence classifiers (RF, LSTM, CNN) achieve only 67.39%, 8.95%, and 7.43% accuracy respectively on 449 classes — near or below random — confirming that size-based traffic analysis features lose nearly all discriminative power against DoH/3.

§IV.D; §V.B; Table I detection

DoHFuse achieves 88.05% accuracy (precision 88.56, recall 87.96, F1 87.83) fingerprinting 449 websites from DoH/3 traffic alone, demonstrating that EDNS(0) padding and QUIC/HTTP3 multiplexing are insufficient to prevent website fingerprinting. In the open-world setting, the model achieves AUPRC 0.975 with F1 0.951 (precision 0.906, recall 1.0), meaning every monitored site is detected while maintaining a low false-positive rate.

Abstract; §V.C; §V.E evaluation

Inter-arrival time sequences between client DoH/3 request packets retain site-specific temporal structure despite QUIC padding and stream multiplexing, exhibiting characteristic micro-burst and inter-burst gap patterns. Using timing features alone, LSTM achieves 83.75% ± 0.92% accuracy at 449 classes, and DoHFuse with its dual-branch DMAG-LSTM reaches 89.08% ± 0.49%, while length-only LSTM degrades to 8.95% under the same conditions.

§IV.D; §V.B; §V.C; Table II detection

Websites generating only one or two observable non-Google DNS queries per visit have classification accuracy below 30% in DoHFuse's closed-world evaluation, compared to the model's overall 88.05% on 449 classes. Sites such as ftc.gov and ailawandorder.com fell near zero accuracy, revealing that extremely sparse DNS activity is a natural but uncontrolled defense against timing-based fingerprinting.

§V.D; Figure 7 evaluation

Chameleon's many-to-many randomized traffic morphing blocks DAAE by eliminating the stable origin→defended-trace mapping that DAAE exploits. Under DAAE-based RF attacks on GTT23, Chameleon limits attack precision to 40.68% and F1-score to 35.19%, compared with 80.64%/70.30% for Palette and 89.82%/88.22% for Adaptive Tamaraw. The defense works by randomly mapping each webpage trace to one of multiple candidate traces, and allowing different webpages to share morphing targets, so no fixed per-class transformation exists for the autoencoder to learn.

2026-cui-chameleon-robust-defense §V-C, Table IV website-fingerprintml-classifiertraffic-shape

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

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.

2026-song-redefining-website-fingerprinting §5.4 / Figure 6 website-fingerprintml-classifiertraffic-shape