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.

Ephemeral defenses were integrated with a WireGuard fork and deployed as Mullvad VPN's 'DAITA' (Defense Against AI-guided Traffic Analysis) opt-in feature across Android, iOS, macOS, Linux, and Windows for over one year, serving a growing number of thousands of daily users. Individual defenses are derived deterministically from seeds in 43.6 ± 4.7 ms on a commodity laptop, making per-connection unique defenses practical at VPN scale.

§1, §7 deployment

Ephemeral blocking defenses reduce DF accuracy from 89.0% (undefended) to 10.2% and RF from 90.1% to 14.7% with standard 30-epoch training, at 97.5% bandwidth and 68.4% delay overhead; under infinite training, DF rises to only 29.2% and RF to 24.3%, still far below undefended baselines of 92.7% and 94.7%. Defenses are tunable at deployment time by adjusting Maybenot framework-wide limits, enabling overhead-vs-protection trade-offs without redeployment.

§5.2, §5.3, Table 1, Table 2 defense

The ephemeral property — using a unique seed-derived defense per connection — prevents attackers from training classifiers on the exact deployed defense variant. Stacked combinations with height H=5 from N=1,000 base defenses yield 6.88×10^25 unique defenses (polynomial growth O(N^{2H})). Attacks trained on ephemeral defenses also generalize significantly better across other randomized defense families than attacks trained on static defenses.

§3.3, §5.4 defense

Padding-only defenses that inject bursty traffic cause severe additional delay under realistic network bottlenecks: Break-Pad's delay overhead increases from 0% to 332.6% and FRONT's from 0% to 111.2% under a per-trace simulated PPS bottleneck. Even ephemeral padding defenses induce 43.9% delay overhead under bottleneck conditions, compared to 0% without a bottleneck, due to congestion from dummy packets.

§5.2, Table 1 evaluation

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

Censorship classifiers and traffic analysis attacks consistently exploit the initial seconds of a proxy connection, where packet-size, inter-arrival-time, and burst features are maximally discriminative. Cited work demonstrates that website fingerprinting classifiers trained solely on the first few seconds of Tor traffic achieve high accuracy, and real-world GFW detection of fully-encrypted protocols also targets early-connection bytes.

2025-pereira-extended §1 traffic-shapewebsite-fingerprintml-classifierdpifully-encrypted-detect

The framework's GAN-based schedule generator trains on short session windows (e.g., the first 10 seconds) of real browsing traffic from the Tranco Top 1000 sites, learning joint distributions of packet sizes, inter-arrival times, and burst patterns to produce realistic synthetic schedules. This repurposes GAN architectures previously used for traffic analysis (e.g., GANDaLF) as a defense-side cover-traffic generator.

2025-pereira-extended §2.2 traffic-shapewebsite-fingerprintml-classifier

Circuit fingerprinting from a guard-relay position achieves ≥99.9% accuracy with FPR ≤0.1% for all four Tor circuit types (general, HSDir, introductory, rendezvous) using the Deep Fingerprinting classifier on the first 512 cells, despite Tor's deployed partial defenses. Onion-Location fingerprinting (OLF) combining these circuit classifiers then achieves 98.81–99.87% accuracy (FPR 0.16–1.23%) distinguishing O-L sessions from ordinary clearnet or onion-only visits.

2025-syverson-onion-location-measurements-fingerprinting §5.1–5.2 traffic-shapeml-classifierwebsite-fingerprint

Combinations of Bayesian methods, data augmentation with mixup, and NOTA defensive padding cut the open-world false positive rate by up to 92% at 0.5 recall on HTTPS-only traffic and 75% on Tor traffic relative to the deterministic MSP baseline. Even with these improvements, sustaining a world size in the hundreds of millions (approaching YouTube-scale) requires accepting recall of 0.5–0.6 and precision of only 0.1–0.2; at precision 0.5 and recall 0.5, the maximum workable world size is only 37.5M for HTTPS-only (Table 3), far below YouTube's ~10 billion video catalog.

2025-walsh-improved-open-world-fingerprinting §4.5, Tables 1–3 website-fingerprintml-classifiertraffic-shape