PSSketch: Finding Persistent and Sparse Flow with High Accuracy and Efficiency

Finding persistent sparse (PS) flow is critical to early warning of various threats. Previous works have predominantly focused on either heavy or persistent flows, with limited attention given to PS flows. Although some recent studies pay attention to PS flows, they struggle to establish an objective criterion due to insufficient data-driven observations, resulting in reduced accuracy. In this paper, we define a new criterion ''anomaly boundary'' to distinguish PS flows from regular flows. Specifically, a flow whose persistence exceeds a threshold will be protected, while a protected flow with a density lower than a threshold is reported as a PS flow. We then introduce PSSketch, a high-precision layered sketch, to find PS flows. PSSketch employs variable-length bitwise counters, where the first layer tracks the frequency and persistence of all flows, and the second layer protects potential PS flows and records overflow counts from the first layer. Some optimizations have also been implemented to reduce memory consumption further and improve accuracy. The experiments show that PSSketch reduces memory consumption by 1-2 orders of magnitude compared to the strawman solution combined with existing work. Compared with SOTA solutions for finding PS flows, it outperforms up to 2.94x higher in F1 score and reduces ARE by 1-2 orders of magnitude. Meanwhile, PSSketch achieves a higher throughput than these solutions.

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Publication Details

Published
2025-08-03
DOI
https://doi.org/10.1145/3711896.3737094
Primary Topic
Caching and Content Delivery
Type
article
Field-Weighted Citation Impact
0.00

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article

PSSketch: Finding Persistent and Sparse Flow with High Accuracy and Efficiency

Jiayao Wang, Qilong Shi, Xiaoping Liang, Shuhui Chen et al.
Caching and Content Delivery
article

PSSketch: Finding Persistent and Sparse Flow with High Accuracy and Efficiency

Jiayao Wang, Qilong Shi, Xiaoping Liang, Shuhui Chen, Ziling Wei, Weizhe Zhang, Wenjun Li, Han Wang
article en

Abstract

Finding persistent sparse (PS) flow is critical to early warning of various threats. Previous works have predominantly focused on either heavy or persistent flows, with limited attention given to PS flows. Although some recent studies pay attention to PS flows, they struggle to establish an objective criterion due to insufficient data-driven observations, resulting in reduced accuracy. In this paper, we define a new criterion ''anomaly boundary'' to distinguish PS flows from regular flows. Specifically, a flow whose persistence exceeds a threshold will be protected, while a protected flow with a density lower than a threshold is reported as a PS flow. We then introduce PSSketch, a high-precision layered sketch, to find PS flows. PSSketch employs variable-length bitwise counters, where the first layer tracks the frequency and persistence of all flows, and the second layer protects potential PS flows and records overflow counts from the first layer. Some optimizations have also been implemented to reduce memory consumption further and improve accuracy. The experiments show that PSSketch reduces memory consumption by 1-2 orders of magnitude compared to the strawman solution combined with existing work. Compared with SOTA solutions for finding PS flows, it outperforms up to 2.94x higher in F1 score and reduces ARE by 1-2 orders of magnitude. Meanwhile, PSSketch achieves a higher throughput than these solutions.

National University of Defense Technology (CN), Harbin Institute of Technology (CN), Nankai University (CN), Peng Cheng Laboratory (CN), Tsinghua University (CN)
Peng Cheng Laboratory, National Natural Science Foundation of China, Government of Jiangsu Province, Jiangsu Provincial Key Research and Development Program
Affordable and clean energy
Openalex Percentile: Top 79%
Caching and Content Delivery
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