A Queueing-Stability Criterion for Causal IPD-QIM Network Flow Watermarking
- URL: http://arxiv.org/abs/2607.14954v2
- Date: Fri, 17 Jul 2026 04:02:30 GMT
- Title: A Queueing-Stability Criterion for Causal IPD-QIM Network Flow Watermarking
- Abstract summary: Causality lets the embedder delay packets but never advance them, so each quantization-index-modulation alignment injects nonnegative dwell into a delay buffer.<n>We model the embedder as a reflected dwell queue under the fixed dual-lattice, equiprobable-bit rule.<n>We give a testable stable-embeddability criterion and a quantization-step configuration baseline for causal QIM network flow watermarking.
- Score: 9.96318277344866
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: On multi-hop encrypted links such as Tor and cascaded VPNs, tunneling flattens packet lengths and protocol fields, leaving inter-packet delay (IPD) as the main carrier for active flow attribution. Causality lets the embedder delay packets but never advance them, so each quantization-index-modulation (QIM) alignment injects nonnegative dwell into a delay buffer; unbounded dwell breaks lattice alignment and delays the host connection unacceptably. Whether a causal QIM watermark embeds stably on bursty traffic has largely been left to empirical configuration rather than analysis. We model the embedder as a reflected dwell queue under the fixed dual-lattice, equiprobable-bit rule, where injection is state-dependent -- set by the current interval and bit -- rather than exogenous. The substitution $Y_i=δ_i-r_i$ gives only an algebraic Lindley-form identity; stability is governed by the busy-state drift at large dwell, where the effective interval collapses to zero and the mean injection becomes $Δ/4$. Away from the critical boundary, the buffer is stable iff $μ_d>Δ/4$ (i.e. $Δ<4μ_d$) for i.i.d. backgrounds, and, under stationary-ergodic and finite-state Markov-modulated traffic with instantaneous overload, iff the time-average intensity $\barρ<1$. With the exogenous decoding floor $Δ\ge cσ_ξ$ ($c=4Q^{-1}(ε/2)$), this yields the operating window $Δ\in[cσ_ξ,4\barμ_d)$. Simulations confirm a sharp transition at $ρ=1$ set only by the mean; on four real IPD traces, with each simulated chain confined to a single flow, the criterion gives the correct stability direction under flow-local correlation and burstiness, while pooled cross-flow means overestimate the margin. These results give a testable stable-embeddability criterion and a quantization-step configuration baseline for causal QIM network flow watermarking.
Related papers
- SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models [53.86918766240095]
Delta-Rule recurrent models maintain a fixed-size state, enabling $O(1)$ inference memory but potentially becoming unstable under extreme-context extrapolation.<n>We propose textbfState Anomaly Neutralization (SANE), which applies adaptive $tanh$ compression at chunk boundaries while preserving the intra-chunk parallel structure.
arXiv Detail & Related papers (2026-08-23T10:41:07Z) - Limit Points of Reflow with Minibatch Optimal Transport [5.12067518301035]
Rectified flows are generative models that learn a time-dependent vector field steering a probability curve between two probability distributions.<n>We study the behavior of this iteration and characterize its limit points.<n>Such $N$-cyclically monotone couplings enjoy favorable structural and stability properties such as rectifiability and straightness.
arXiv Detail & Related papers (2026-08-07T09:51:33Z) - Anisotropic Template Ansätze for Robust Positive Invariance under State-Dependent Uncertainty [1.2234742322758416]
We map a fixed ellipsoidal template through a GP-derived positive-definite matrix field.<n>The resulting LMI conditions couple the learned field to Schur-stable dynamics.<n> Quadrotor simulations show a $195times$ reduction in 3D velocity-tube volume.
arXiv Detail & Related papers (2026-06-14T23:52:29Z) - Metric--Phase Fields: Decoupling Distance and Sign for Thin-Structure Reconstruction from Unoriented Point Clouds [67.30236447078187]
We introduce Metric-Phase Fields (MPFs), a decoupled implicit representation that separates metric proximity from topological phase.<n>Experiments on both synthetic and scanned thin-shell and thin-plate shapes demonstrate that MPFs preserve thin and layered structures more faithfully than recent SDF-based methods.
arXiv Detail & Related papers (2026-05-25T07:08:57Z) - Multi-Fidelity Flow Matching: Cascaded Refinement of PDE Solutions [6.144605787421899]
Multi-Fidelity Flow Matching is a cascade refinement framework for PDE solutions.<n>We exploit this in Multi-Fidelity Flow Matching (MFFM), a cascade refinement framework for PDE solutions.<n>MFFM is a learned analog of multigrid refinement that reaches the finest grid in $L$ deterministic network evaluations per query.
arXiv Detail & Related papers (2026-05-15T16:02:18Z) - Sequential vs. Simultaneous Entanglement Swapping under Optimal Link-Layer Control [1.8829359083448898]
Connection-less, packet-switched quantum network architectures distribute entanglement across multi-hop paths through sequential entanglement swapping.<n>We present a proof-of-principle study at fixed chain length $n = 4$ in which each link is governed by a fixed reinforcement-learning policy.<n>Sequence swapping collapses to zero end-to-end deliveries below $T_cmathrmext/= 25$, and begins recovering at $T_cmathrmext/= 50$.
arXiv Detail & Related papers (2026-05-05T17:59:22Z) - Riemannian MeanFlow for One-Step Generation on Manifolds [54.09734511705173]
Flow Matching enables simulation-free training of generative models.<n>MeanFlow can be extended to manifold-valued generation where velocities lie in location-dependent tangent spaces.<n>Experiments on spheres, tori, and SO(3) demonstrate competitive one-step sampling with improved quality-efficiency trade-offs and substantially reduced sampling cost.
arXiv Detail & Related papers (2026-03-11T12:41:46Z) - Network-Optimised Spiking Neural Network for Event-Driven Networking [2.5941336499463383]
Spiking neural networks offer event-driven computation suited to time-critical networking tasks such as anomaly detection, local routing control, and congestion management at the edge.<n>We introduce Network-Optimised Spiking (NOS), a compact two-variable unit whose state encodes normalised queue occupancy and a recovery resource.<n>We provide guidance for data-driven initialisation, surrogate-gradient training with a homotopy on reset sharpness, and explicit stability checks with topology of bounds for resource constrained deployments.
arXiv Detail & Related papers (2025-09-27T22:31:24Z) - DASA: Delay-Adaptive Multi-Agent Stochastic Approximation [64.32538247395627]
We consider a setting in which $N$ agents aim to speedup a common Approximation problem by acting in parallel and communicating with a central server.
To mitigate the effect of delays and stragglers, we propose textttDASA, a Delay-Adaptive algorithm for multi-agent Approximation.
arXiv Detail & Related papers (2024-03-25T22:49:56Z) - Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling [73.5602474095954]
We study the non-asymptotic performance of approximation schemes with delayed updates under Markovian sampling.
Our theoretical findings shed light on the finite-time effects of delays for a broad class of algorithms.
arXiv Detail & Related papers (2024-02-19T03:08:02Z) - Simulation-free Schr\"odinger bridges via score and flow matching [89.4231207928885]
We present simulation-free score and flow matching ([SF]$2$M)
Our method generalizes both the score-matching loss used in the training of diffusion models and the recently proposed flow matching loss used in the training of continuous flows.
Notably, [SF]$2$M is the first method to accurately model cell dynamics in high dimensions and can recover known gene regulatory networks simulated data.
arXiv Detail & Related papers (2023-07-07T15:42:35Z) - Deep Momentum Multi-Marginal Schr\"odinger Bridge [41.27274841596343]
We present a novel framework that learns the smooth measure-valued algorithm for systems that satisfy position marginal constraints across time.
Our algorithm outperforms baselines significantly, as evidenced by experiments for synthetic datasets and a real-world single-cell RNA dataset sequence.
arXiv Detail & Related papers (2023-03-03T07:24:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.