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

Computer Science > Cryptography and Security [Submitted on 10 Jun 2026] Hiding the Trees in the Forest: Building Network Covert Channels with Hash-Based Covert Carrier Filtering Zexiao Zou, Zhiqiang Wang, Baoxu Liu, Yuyang Han, Yan Zhang 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 \mu s, indicating the feasibility of deploying the proposed strategy in high-speed network environments. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.11532 [cs.CR]   (or arXiv:2606.11532v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.11532 Focus to learn more Submission history From: Zhiqiang Wang [view email] [v1] Wed, 10 Jun 2026 00:29:26 UTC (12,196 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)