A Comprehensive Analysis of Interval Based Network Flow Watermarking

Shi, Jin; Zhang, Li; Yin, Shuijun; Liu, Weiwei; Zhai, Jiangtao; Liu, Guangjie; Dai, Yuewei

doi:10.1007/978-3-030-00015-8_7

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…We select three aspects to evaluate the performance of several flow watermarking techniques [32]: robustness, invisibility and coding efficiency. Robustness refers to the ability to keep the original data when facing network delays, packet loss and other disturbances.…”

Section: Network Flow Watermarkingmentioning

confidence: 99%

A Fine-Grained Network Congestion Detection Based on Flow Watermarking

Wang

2022

Applied Sciences

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With the rapid development of the network, how to effectively reduce the dynamic delay and improve the performance of the network is an important and challenging problem. Specifically, network congestion is one of the key factors that hurt the network performance, so real-time detection of the network congestion is critical for recovering the network failure quickly. Current research in congestion detection mainly faces the problems of occupying extra bandwidth, decreasing the ratio of the effective payload of the packet, increasing the burden of the switches, etc. In this paper, we apply flow watermarking to network congestion detection and propose a fine-grained network congestion detection method based on flow watermarking. We also combine it with the eBPF (extended Berkeley Packet Filter) to improve the performance of congestion detection. Theoretical analysis and experimental results show that the changes in network status can be reflected in real-time and accurately in the watermark decoding information. The network congestion detection based on flow watermarking can detect network status on a small time scale and realize low-overhead and easily deployed congestion detection.

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Section: Network Flow Watermarkingmentioning

confidence: 99%