Hiding the Trees in the Forest: Building Network Covert Channels with Hash-Based Covert Carrier Filtering

• URL: http://arxiv.org/abs/2606.11532v1
• Date: Wed, 10 Jun 2026 00:29:26 GMT
• Title: Hiding the Trees in the Forest: Building Network Covert Channels with Hash-Based Covert Carrier Filtering
• Abstract summary: This paper proposes a covert carrier filtering strategy based on the hash.<n>We experimentally validate the strategy in two types of network covert channels: network storage and timing covert channels.
• Score: 8.925795239005074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an effective anti-censorship mechanism, network covert channels can provide data privacy protection and ensure communication security. However, the covertness of existing network covert channels primarily depends on the secrecy of their covert algorithms. With the increasing depth of research in this field, the difficulty of breaking such algorithms has gradually decreased. Once the algorithm is exposed, the network covert channel can be easily detected by adversaries. To address this issue, this paper proposes a covert carrier filtering strategy based on the hash. In this strategy, a key-dependent filtering rule is introduced during the construction of the network covert channel, enabling the communicating parties to randomly and dynamically filter a sparse subset from the carrier set as the covert carrier set. This strategy not only enhances the randomness of carrier selection but also tightly couples the covertness of the network covert channel with the security of the key. We employ machine learning-based traffic analysis methods to experimentally validate the strategy in two types of network covert channels: network storage and timing covert channels. The experimental results demonstrate that the proposed strategy significantly improves the detection resistance of network covert channels. When the filter key size exceeds six bits, the impact on the detection effect of the classifier becomes quite significant. Furthermore, the processing delay for a single packet is less than 8 $μs$, indicating the feasibility of deploying the proposed strategy in high-speed network environments.

Related papers

• 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)
• Blockwise Post-processing in Satellite-based Quantum Key Distribution [49.84121978591396]
  We argue that improving classical post-processing is an important direction in increasing key rate in satellite-based QKD.<n>In particular, we explore one direction, blockwise post-processing, to address highly dynamic satellite channel conditions.
  arXiv  Detail & Related papers  (2025-03-08T03:06:04Z)
• CONNECTION: COvert chaNnel NEtwork attaCk Through bIt-rate mOdulatioN [1.7034813545878589]
  Covert channel networks are a well-known method for circumventing the security measures organizations put in place to protect their networks from adversarial attacks. This paper introduces a novel method based on bit-rate modulation for implementing covert channels between devices connected over a wide area network.
  arXiv  Detail & Related papers  (2024-04-24T13:14:09Z)
• Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks [26.225658457052834]
  In space-air-ground integrated networks (SAGIN), the inherent openness and extensive broadcast coverage expose these networks to significant eavesdropping threats. It is challenging to conduct a secrecy-oriented access strategy due to both heterogeneous resources and different eavesdropping models. We propose a Q-network approximation based deep learning approach for selecting the optimal access strategy for maximizing the sum secrecy rate.
  arXiv  Detail & Related papers  (2023-08-28T06:48:06Z)
• LGTBIDS: Layer-wise Graph Theory Based Intrusion Detection System in Beyond 5G [9.63617966257402]
  Intrusion detection signifies a central approach to ensuring the security of the communication network. A Layerwise Graph Theory-Based Intrusion Detection System (LGTBIDS) algorithm is designed to detect the attacked node. Results validate the better performance, low time computations, and low complexity.
  arXiv  Detail & Related papers  (2022-10-06T05:32:03Z)
• Group Fisher Pruning for Practical Network Compression [58.25776612812883]
  We present a general channel pruning approach that can be applied to various complicated structures. We derive a unified metric based on Fisher information to evaluate the importance of a single channel and coupled channels. Our method can be used to prune any structures including those with coupled channels.
  arXiv  Detail & Related papers  (2021-08-02T08:21:44Z)
• Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks [67.7648985513978]
  Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. We present a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism.
  arXiv  Detail & Related papers  (2021-07-19T12:27:40Z)
• Adversarial Attack on Network Embeddings via Supervised Network Poisoning [20.583791500831147]
  We study network embedding algorithms from an adversarial point of view and observe the effect of poisoning the network on downstream tasks. We propose VIKING, a supervised network poisoning strategy that outperforms the state-of-the-art poisoning methods by upto 18% on the original network structure.
  arXiv  Detail & Related papers  (2021-02-14T14:47:39Z)
• Attribution Preservation in Network Compression for Reliable Network Interpretation [81.84564694303397]
  Neural networks embedded in safety-sensitive applications rely on input attribution for hindsight analysis and network compression to reduce its size for edge-computing. We show that these seemingly unrelated techniques conflict with each other as network compression deforms the produced attributions. This phenomenon arises due to the fact that conventional network compression methods only preserve the predictions of the network while ignoring the quality of the attributions.
  arXiv  Detail & Related papers  (2020-10-28T16:02:31Z)
• SCOP: Scientific Control for Reliable Neural Network Pruning [127.20073865874636]
  This paper proposes a reliable neural network pruning algorithm by setting up a scientific control. Redundant filters can be discovered in the adversarial process of different features. Our method can reduce 57.8% parameters and 60.2% FLOPs of ResNet-101 with only 0.01% top-1 accuracy loss on ImageNet.
  arXiv  Detail & Related papers  (2020-10-21T03:02:01Z)

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.