The detection event on Beeline (AS16345) exhibits a 5–15 minute delay between TUN activation and connection degradation onset, followed by a further delay before full blocking, consistent with a behavioral or statistical classifier rather than a signature-based real-time inspector. The graduated timeline (partial degradation → near-total block) suggests a threshold-based scoring system rather than an immediate signature match.

After the detection event triggers on Beeline (AS16345), the connection is restricted to exactly one destination: the government-approved MAX messenger (max.ru), while all other Russian domestic whitelist domains (ok.ru, vk.ru, ya.ru, mail.ru) are also blocked. This response is enforced at the ISP/TSPU level and persists until the LTE router is restarted, indicating a stateful per-subscriber penalty state rather than simple packet-level blocking.

Issue body deployment

On Beeline mobile internet (AS16345, Russia), enabling TUN mode with a VLESS+REALITY proxy consistently triggers ISP-level connection degradation after 5–15 minutes, while using the same proxy in HTTP/SOCKS5 proxy mode produces no such effect. The detection is reproducible across two independent GUI clients (v2rayN and Throne) with stock settings, suggesting the DPI trigger is the TUN traffic pattern rather than a client-specific artifact.

Issue body detection

The reporter notes the phenomenon began several months prior to the September 2026 report and has only one corroborating data point (a Telegram message describing similar Beeline behavior), suggesting the TSPU capability is narrowly deployed — possibly in a single region or in a trial phase — rather than nationally rolled out. The reporter explicitly flags uncertainty about whether this is a TSPU experiment or a misconfiguration in their VPN client.

Issue body evaluation

The TUN-mode detection occurs exclusively on Windows 10 and does not manifest on Android using the same VLESS+REALITY server and Beeline SIM, nor when running as a system proxy on Windows. The effect is also observed to impact all devices sharing the Windows hotspot simultaneously, ruling out endpoint-local causes and confirming the classification occurs upstream at the ISP/TSPU layer.

Issue body detection

Across eight combinations of traffic features (packet length, bi-gram packet length, inter-packet time, bi-gram inter-packet time) and two similarity metrics (EMD, KS), adversarial classification accuracy against DeltaShaper streams ranges from 72–90% in unperturbed conditions. Bi-gram inter-packet times with EMD achieves 88% accuracy, matching packet-length/EMD, but requires roughly 10x the computation (~64s vs ~6s). Bandwidth throttling to 300 Kbps degrades classifier accuracy from 88% to 75%, but also drops Skype frame rate from 30 to 5 FPS, creating collateral damage that limits censor deployment of throttling as a detection aid.

2017-barradas-deltashaper §6.4, §6.5, Table 3 traffic-shapeml-classifierthrottling

Based on analysis of over 100,000 leaked Geedge Networks documents, InterSecLab (2025) found that Geedge supplied China-GFW-equivalent censorship and surveillance systems to Kazakhstan, Ethiopia, Pakistan, and Myanmar. The systems include deep packet inspection, real-time mobile user monitoring, fine-grained traffic control, regionally customizable censorship rules, and — per WIRED's reporting — ML-based extraction of metadata from encrypted traffic to determine whether a flow is likely from a VPN or circumvention tool.

2026-anon-2026-27-vpn §8 (InterSecLab / WIRED section) dpiml-classifierfully-encrypted-detecttraffic-shape

Existing Provably Secure Steganography (PSS) schemes require access to the explicit output distribution of the generative model, a requirement that is impractical for black-box API deployments. This paper demonstrates that computational indistinguishability between normal and steganographic output can be achieved using only a seed-based model API, with no distribution access required.

2026-bai-provable-secure-steganography Abstract / §1 Introduction traffic-shapeml-classifier

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

NATA (Non-invasive Active Traffic-correlation Analysis) injects low-frequency bandwidth waveforms (sinusoidal, square-wave, triangular) into Tor TCP connections at an upstream gateway without endpoint compromise, payload decryption, or Tor-browser modification. BM-Net, a selective state-space classifier trained on the exit-side observations, achieves a 99.65% binary detection F1 score distinguishing watermarked from natural traffic on a 20,000-trace real-world dataset.

2026-fan-activeflowmark-assessing-tor §IV, §VI.D, Table III flow-correlationtraffic-shapeml-classifier

BM-Net achieves a 99.65% binary detection F1 score for distinguishing bandwidth-watermarked Tor flows from natural traffic, outperforming all evaluated baselines (next best: TikTok at 75.96% F1). The accuracy gap stems from active perturbation imposing a deterministic low-frequency throughput constraint rather than relying on subtle natural metadata, making the detection task fundamentally easier than passive website fingerprinting.

2026-fan-activeflowmark-assessing-tor §VI-D, Table III–IV flow-correlationtraffic-shapeml-classifier