Silence Speaks Volumes: A New Paradigm for Covert Communication via History Timing Patterns

• URL: http://arxiv.org/abs/2511.22259v1
• Date: Thu, 27 Nov 2025 09:34:36 GMT
• Title: Silence Speaks Volumes: A New Paradigm for Covert Communication via History Timing Patterns
• Authors: Christoph Weissenborn, Steffen Wendzel,
• Abstract summary: History Covert Channels (HCC) leverage past network events as reference points to embed covert messages.<n>HCCs minimize detectability by encoding information through small pointers to historical data.<n>This paper introduces a novel method for establishing and maintaining covert communication links using relative pointers to network timing patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A Covert Channel (CC) exploits legitimate communication mechanisms to stealthily transmit information, often bypassing traditional security controls. Among these, a novel paradigm called History Covert Channels (HCC) leverages past network events as reference points to embed covert messages. Unlike traditional timing- or storage-based CCs, which directly manipulate traffic patterns or packet contents, HCCs minimize detectability by encoding information through small pointers to historical data. This approach enables them to amplify the size of transmitted covert data by referring to more bits than are actually embedded. Recent research has explored the feasibility of such methods, demonstrating their potential to evade detection by repurposing naturally occurring network behaviors as a covert transmission medium. This paper introduces a novel method for establishing and maintaining covert communication links using relative pointers to network timing patterns, which minimizes the reliance of the HCC on centralized timekeeping and reduces the likelihood of being detected by standard network monitoring tools. We also explore the tailoring of HCCs to optimize their robustness and undetectability characteristics. Our experiments reveal a better bitrate compared to previous work.

Related papers

• Send Less, Perceive More: Masked Quantized Point Cloud Communication for Loss-Tolerant Collaborative Perception [38.10779821259225]
  We introduce QPoint2Comm, a quantized point-cloud communication framework that dramatically reduces bandwidth.<n>QPoint2Comm directly communicates quantized point-cloud indices using a shared codebook.<n>We employ a masked training strategy that simulates random packet loss, allowing the model to maintain strong performance even under severe transmission failures.
  arXiv  Detail & Related papers  (2026-02-25T08:00:48Z)
• Joint Source-Channel-Generation Coding: From Distortion-oriented Reconstruction to Semantic-consistent Generation [58.67925548779465]
  We propose Joint Source-Channel-Generation Coding (JSCGC), a novel paradigm that shifts the focus from perceptual reconstruction to probabilistic generation.<n>JSCGC improves substantially semantic quality and semantic fidelity, significantly outperforming conventional distortion-oriented J SCC methods.
  arXiv  Detail & Related papers  (2026-01-19T08:12:47Z)
• Generative MIMO Beam Map Construction for Location Recovery and Beam Tracking [67.65578956523403]
  This paper proposes a generative framework to recover location labels directly from sparse channel state information (CSI) measurements.<n>Instead of directly storing raw CSI, we learn a compact low-dimensional radio map embedding and leverage a generative model to reconstruct the high-dimensional CSI.<n> Numerical experiments demonstrate that the proposed model can improve localization accuracy by over 30% and achieve a 20% capacity gain in non-line-of-sight (NLOS) scenarios.
  arXiv  Detail & Related papers  (2025-11-21T07:25:49Z)
• CYPRESS: Transferring Secrets in the Shadow of Visible Packets [2.626352787008701]
  We show that covert channels in networking have a much greater potential for practical secret communication than what has been discussed before.<n>We present a covert channel framework, CYPRESS, that creates a reliable hidden communication channel by mounting packets from secret network entities on regular packets that flow through the network.<n>We can reach up to 1.6MB/s of secret bandwidth in a network of ten users connected to the Internet.
  arXiv  Detail & Related papers  (2025-11-09T21:02:09Z)
• SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks [73.41290017870097]
  SecDiff is a plug-and-play, diffusion-aided decoding framework.<n>It significantly enhances the security and robustness of deep J SCC under adversarial wireless environments.
  arXiv  Detail & Related papers  (2025-11-03T11:24:06Z)
• WavInWav: Time-domain Speech Hiding via Invertible Neural Network [78.85443308774484]
  Previous audio hiding methods often result in unsatisfactory quality when recovering secret audio.<n>We use a flow-based invertible neural network to establish a direct link between stego audio, cover audio, and secret audio.<n>We also add an encryption technique to protect the hidden data from unauthorized access.
  arXiv  Detail & Related papers  (2025-10-03T11:36:16Z)
• Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach [65.47969413708344]
  We introduce the concept of CF twins and design a conditional generative diffusion model (CGDM)<n>We employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CF conditioned on the coarse-grained CF.<n>We show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines.
  arXiv  Detail & Related papers  (2025-05-12T01:36:06Z)
• Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs [6.059175509501795]
  Joint source-channel coding (JSCC) offers an alternative end-to-end approach by optimizing compression and channel coding together. This article provides an overview of the information theoretic foundations of J SCC, surveys practical J SCC designs over the decades, and discusses the reasons for their limited adoption in practical systems.
  arXiv  Detail & Related papers  (2024-09-26T06:10:29Z)
• Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks [31.408649975934008]
  Time-Series Joint Embedding Predictive Architecture (TS-JEPA)<n>TS-JEPA encodes high-dimensional sensory data into low-dimensional semantic embeddings at the sensor.<n>Channel-aware scheduling is integrated to dynamically prioritize device transmissions.
  arXiv  Detail & Related papers  (2024-06-07T11:35:15Z)
• Privacy-Aware Joint Source-Channel Coding for image transmission based on Disentangled Information Bottleneck [27.929075969353764]
  Current privacy-aware joint source-channel coding (JSCC) works aim at avoiding private information transmission by adversarially training the J SCC encoder and decoder. We propose a novel privacy-aware J SCC based on disentangled information bottleneck (DIB-PAJSCC) We show that DIB-PAJSCC can reduce the eavesdropping accuracy on private information by up to 20% compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-15T06:34:22Z)
• Secure Deep-JSCC Against Multiple Eavesdroppers [13.422085141752468]
  We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers. We implement deep neural networks (DNNs) to realize a data-driven secure communication scheme. Our experiments show that employing the proposed secure neural encoding can decrease the adversarial accuracy by 28%.
  arXiv  Detail & Related papers  (2023-08-05T14:40:35Z)
• Practical quantum secure direct communication with squeezed states [37.69303106863453]
  We report the first table-top experimental demonstration of a CV-QSDC system and assess its security.<n>This realization paves the way into future threat-less quantum metropolitan networks, compatible with coexisting advanced wavelength division multiplexing (WDM) systems.
  arXiv  Detail & Related papers  (2023-06-25T19:23:42Z)
• GCN for HIN via Implicit Utilization of Attention and Meta-paths [104.24467864133942]
  Heterogeneous information network (HIN) embedding aims to map the structure and semantic information in a HIN to distributed representations. We propose a novel neural network method via implicitly utilizing attention and meta-paths. We first use the multi-layer graph convolutional network (GCN) framework, which performs a discriminative aggregation at each layer. We then give an effective relaxation and improvement via introducing a new propagation operation which can be separated from aggregation.
  arXiv  Detail & Related papers  (2020-07-06T11:09:40Z)

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.