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A survey on network flow watermarking: A problem-oriented perspective
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DOI：10.1016/j.cose.2025.104813.png)
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摘要
En 中文
Network flow watermarking is an active tracing approach by embedding the source node identity information into network flows invisibly. The embedded watermarks coexist with the original network traffic, which are designed with robustness against network noises and active interferences. In recent years, network flow watermarking enters a phase of profound development in face of various challenges on practical applications (e.g. deanonymization, data leakage tracing, malicious behavior monitoring) in Internet. However, to our best knowledge, there are very few surveys for the network flow watermarking methods after 2018, for which the systematic survey covering the entire developments is absent. Meanwhile, current surveys classify and analyze network flow watermarking based on embedding patterns, which ignore different methods correlations on critical problems for watermarking. To this end, the paper reviews and analyzes the papers from 2001 to 2025 on network flow watermarking in perspective of problem-orientations. The threat model and theoretical framework are established to model the watermarking embedding and detecting procedures, which offer a consistent model for watermarking design. From three core problems on robustness, invisibility and adaptation, network flow watermarking methods are classified into different solutions to these problems, which depict an explicit network flow watermarking development roadmap. For sake of facilitating practical applications, five open problems as the critical challenges are discussed, providing references for the future work on network flow watermarking.
期刊
C
IF:
5.4
论文数:
314
被引数:
0
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引用论文
Qiao, S.; Guo, Q.; Shi, F.; Wang, M.; Zhu, H.; Khan, F.; Rodrigues, J.J.P.C.; Lyu, Z. SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital Healthcare Systems. IEEE J. Biomed. Health Inform. 2025, 1–12. [Google Scholar] [CrossRef]乔, S.; 郭, Q.; 史, F.; 王, M.; 朱, H.; 卡汉, F.; 罗德里格斯, J.J.P.C.; 吕, Z. SIBW: 一种基于群智能的网络流量水印方法，用于数字医疗系统中的隐私泄露检测。IEEE 生物医学与健康信息学杂志, 2025, 1–12. [谷歌学术] [CrossRef].png)
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