At filter key length L=6, approximately 1.56% of packets are selected as covert carriers, yielding a unit covert capacity (packets required per covert bit) of 3.1–8 depending on bits embedded per packet. Each additional filter key bit halves the carrier selection rate (r = 1/2^L), so L=8 selects 0.39% of packets and pushes unit covert capacity above 32.

The hash-based filtering strategy shifts covert channel security from algorithm secrecy to key secrecy per Kerckhoffs's principle: even when the covert algorithm is fully exposed, an adversary without the pre-shared key cannot identify which sparse subset (~1/2^L) of packets carries covert data, transforming the detection problem from statistical pattern recognition to exhaustive key-space search. The authors recommend a minimum Input Key length of 128 bits.

§3.2, §4.1, §4.2.4 defense

The filtering strategy introduces no measurable increase in bit error rate (BER) or reduction in effective transmission rate (ETR) under packet loss rates up to 15%, reordering rates up to 20%, and delay jitter up to 100±50 ms, because the filter only selects carriers — data embedding and extraction logic remain identical to unfiltered channels.

§5.3.1, Figure 5 evaluation

Per-packet processing overhead for SHA-256-based covert carrier filtering remains below 8 µs across all tested filter key sizes (L=1–8): storage channels (709-byte average payload) take 5.7–6.48 µs; timing channels (1146-byte average payload) take 7.44–7.87 µs, measured on a 12th-gen Intel Core i5-12400 at 2.50 GHz. This overhead does not cause packet loss or measurable end-to-end latency increase.

§5.3.3, Table 5 evaluation

Introducing hash-based covert carrier filtering does not affect bit error rate (BER) or effective transmission rate (ETR) of the underlying covert channel under packet loss up to 15%, reordering up to 20%, or delays up to 100±50 ms, because the filtering layer only governs carrier selection and leaves embedding and extraction logic unchanged. Results for L=0 and L=6 were statistically indistinguishable across all tested network impairment conditions.

§5.3.1, Fig.5 evaluation

Hash-based covert carrier filtering reduces ML classifier AUC for network storage covert channels from 1.0 (no filtering, L=0) to well below 0.7 when filter key size L≥6; SVM detection approaches random-guessing levels. The strategy selects a sparse, pseudo-random 1/2^L subset of packets as covert carriers using SHA-256 keyed by a pre-shared secret, diluting statistical anomalies in header field modifications with a large volume of unmodified legitimate packets.

§5.3.2, Fig.6 defense

For network timing covert channels, hash-based covert carrier filtering reduces ML classifier AUC from 0.99 (L=0) to below 0.6 for all three classifiers (SVM, XGBoost, DT) at filter key size L=5, indistinguishable from the baseline normal-traffic AUC of 0.55. Sparse, aperiodic timing modulation events at L≥5 are masked by the network's inherent jitter, eliminating the autocorrelation and regularity signatures that standard timing-channel detectors exploit.

§5.3.2, Fig.7 defense

The covert carrier filtering strategy shifts channel covertness from algorithm secrecy to key strength: once the embedding algorithm is exposed, an adversary must exhaustively search a 2^L key space to enumerate the covert carrier set rather than applying pattern recognition. At L=6, only 1/64 (~1.56%) of candidate packets are selected as covert carriers, yielding unit covert capacity Ucc=8 per packet for 8-bit embeddings and Ucc=3.1 for 21-bit embeddings.

§3.2, §4.2.2, Table 6 defense

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

BM-Net achieves a 99.65% binary detection F1 score distinguishing watermarked from natural Tor flows, and a 97.5% macro-F1 score for fine-grained modulation classification across sinusoidal, square-wave, and triangular patterns. The fine-grained test set contains 201 held-out samples collected from ten clients across five geographic regions (Europe, North America, Australia, Southeast Asia, East Asia), with training traces including traffic collected under WTF-PAD and Walkie-Talkie defenses.

2026-fan-activeflowmark-assessing-tor §VI-D, Table III, Table V flow-correlationml-classifiertraffic-shape