Dodge-mimic reduces vRF deep-learning classifier accuracy to 10.6–15.6% for k=10 anonymity sets, matching the theoretical k-anonymity bound of (100/k)%=10%. The heuristic Walls Have Ears attack also fails: WHE-seg peaks at ≤8.5% and WHE-cyk at 9.7%, confirming that equalized cycle sizes leak no per-video information.

In live deployment with k=10 anonymity sets, video+audio+ABR, Dodge-mimic causes zero playback stalls in approximately 68% of sessions; for the remaining 32%, the mean initial stall time is 1.6 seconds — an extension of normal startup delay rather than mid-playback interruption — and all cycles complete roughly 20 seconds before playback ends even with large trailing cycles.

§5.3, §5.3.3 evaluation

Small random per-session perturbations (±bytes per range) to Dodge-mimic extended manifests fully compensate for HPACK header compression in HTTP/2, yielding vRF accuracy of 10.5–14.2% for k=10 — indistinguishable from HTTP/1.1 results and near the theoretical bound. Because HTTP/3-QUIC uses analogous QPACK compression, the approach generalizes to the dominant future transport.

§5.4, Table 3 defense

Even with perfectly equalized cycle sizes, a persistent intra-response timing side channel survives in Dodge: timing characteristics of individual packets within server responses cannot be controlled client-side, causing vRF accuracy to remain slightly above random guessing in all tested scenarios. This residual leak is consistent and observable regardless of anonymity set size or media configuration.

§5.2.1, §6 detection

With 32 worker threads, Dodge-mimic anonymity-set bandwidth overhead (as percentage of undefended video content) ranges from 21.0%±0.8 at k=2 to 73.1%±2.8 at k=10 and 107.4%±1.5 at k=20; beyond k=10–16 threads, additional parallelism yields diminishing returns and can increase overhead due to thread-boundary fragmentation of the optimal search space.

§5.1, Table 1 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

In controlled Testnet conditions, the Tik-Tok deep-learning WF attack achieves F1=0.87 (closed-world) and F1=0.83 (open-world) against Nym's default configuration, compared to F1=0.94/0.91 on Tor. On the live Mainnet, real-world network jitter reduces attack effectiveness to F1=0.69/0.71, but this comes at 11.93× latency overhead and 8.97× bandwidth overhead — a side-effect of instability rather than designed protection. The Mainnet drop is attributable to geographic node spread and roughly 5% packet retransmissions rather than to designed obfuscation.

2026-joll-s-website-fingerprinting-nym §5.1, Table 2 website-fingerprintml-classifier

Routing web traffic through Nym's constant-rate traffic stream (POISS-OFF) reduces Tik-Tok F1 to 0.06, with all feature-set leakage at ≤9.5% accuracy (Table 8), at the cost of latency overhead 7.32 and bandwidth overhead 8.06 (Table 7). Nym's 2,413-byte packet size means each cover packet incurs 4.7× the overhead of a Tor cover cell, making constant-rate defenses significantly more expensive in this architecture. This matches near-ideal Tamaraw protection on Tor, against WTF4Nym's F1 = 0.39 at 3.78x latency and 4.22x bandwidth.

2026-joll-s-website-fingerprinting-nym §6, Tables 7–8 website-fingerprintml-classifier

WTF4Nym, a burst-aware cover-traffic defense adapted to Nym's Poisson-rate architecture, reduces the Tik-Tok attack F1 score from 0.87 to 0.39 at moderate overhead (latency overhead 3.78×, bandwidth overhead 4.22×). This substantially outperforms directly applying WTF-PAD to Nym (F1=0.87), applying FRONT to Nym (F1=0.63), and the F1=0.65 achieved by FRONT on Tor at comparable overhead, by learning per-network burst distributions and injecting fake bursts that match those statistics. FRONT on Tor reaches F1 = 0.65 at 2.79x latency and 1.84x bandwidth, and under WTF4Nym the Time and N-gram feature leakage fall to 26.6% and 29.0% (Table 8).

2026-joll-s-website-fingerprinting-nym §6.2, Table 7 website-fingerprintml-classifier

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

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.

2026-pulls-ephemeral-network-layer-fingerprinting §1, §7 ml-classifierwebsite-fingerprint