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.

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

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

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.

§5.3, Table 2 evaluation

Without chunk-based padding, an XGBoost classifier identifies the target website from covert data-chunk sizes with 91% accuracy (Tranco top-100). Chunking at 2 MB reduces accuracy to 12% at a 21.3% bandwidth overhead, while 16 MB chunks reduce accuracy to near random guessing at a 480.3% overhead. Chunks as small as 64 KB already reduce accuracy to 64%, demonstrating a monotonic fingerprinting–overhead tradeoff.

2026-kamali-huma §V-C, Figure 4 website-fingerprintml-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

In the open-world 5-tab setting — where each trace contains one unmonitored site, substantially increasing noise and class imbalance — DEMUX achieves AUC of 0.998, P@5 of 0.951, and MAP@5 of 0.966, while ARES'25 achieves 0.988/0.869/0.911. DEMUX's advantage widens in the open-world setting (the P@5 gap grows from 2.6 pp to 8.2 pp versus closed-world), confirming that state-of-the-art WF attacks are not defeated by open-world conditions or unmonitored co-browsing traffic.

2026-yuan-demux-boundary-aware-multi-scale §V-C, Table III website-fingerprintml-classifier

The Traffic Aggregation Matrix (TAM) representation used by the RF baseline — which counts directional packet counts over fixed time slots rather than tracking per-packet sequences — shows unexpectedly strong robustness under TrafficSliver, achieving P@2 of 0.702, substantially exceeding all other CNN-based methods under that defense. Var-CNN similarly achieves P@2 of 0.826 under TrafficSliver despite mediocre no-defense performance, suggesting that tolerance to partial packet loss is architecturally separable from peak single-observer accuracy.

2026-yuan-demux-boundary-aware-multi-scale §V-D, Table IV website-fingerprintml-classifier

Under the TrafficSliver defense — which splits traffic across multiple Tor entry nodes so no single observer sees more than a partial fraction of packets — TMWF collapses to a P@2 of 0.399 and ARES'23 to 0.429, while DEMUX retains a P@2 of 0.940, exceeding the next-best competitor by 2.5 points. WTF-PAD and FRONT are substantially weaker defenses, with most methods maintaining near-baseline performance under WTF-PAD.

2026-yuan-demux-boundary-aware-multi-scale §V-D, Table IV website-fingerprintml-classifier