Name
Abstract | ||
|---|---|---|
Flow watermarking is a promising active traffic tracking technology. It helps to establish the correspondence between the sender and the receiver, by embedding watermarks into packets with certain features of active interference traffic. Existing watermarking technologies have several drawbacks, such as the vulnerability to multi-flow attacks, low robustness and invisibility. This paper proposes a new traffic tracking technique based on hidden Markov model, called Hidden Markov State-based Flow Watermarking (HMSFW). HMSFW divides the observation range, i.e., inter-packet time, as Markov states and uses the State Transition Probability Mean (STPM) as the watermark carrier. It then adjusts the STPM of a given time interval according to historical traffic characteristics. The proposed HMSFW not only improves the robustness of embedded watermarks, but also effectively enhances their invisibility. The feasibility and invisibility of HMSFW are validated via extensive experimental results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICC.2019.8761135 | IEEE International Conference on Communications |
Field | DocType | ISSN |
Data mining,Digital watermarking,Embedding,Computer science,Network packet,Markov chain,Robustness (computer science),Watermark,Real-time computing,Hidden Markov model,Invisibility | Conference | 1550-3607 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongjiang Yao | 1 | 7 | 1.80 |
| lei zhang | 2 | 403 | 143.70 |
| Jingguo Ge | 3 | 7 | 3.15 |
| Yulei Wu | 4 | 480 | 51.95 |
| Xiaodan Zhang | 5 | 2 | 3.41 |