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

  • Jiayao Wang
  • , Qilong Shi
  • , Xiyan Liang
  • , Han Wang
  • , Wenjun Li*
  • , Ziling Wei
  • , Weizhe Zhang
  • , Shuhui Chen*
  • *Corresponding author for this work
  • National University of Defense Technology
  • Tsinghua University
  • Nankai University
  • Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationKDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2950-2961
Number of pages12
ISBN (Electronic)9798400714542
DOIs
StatePublished - 3 Aug 2025
Event31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025 - Toronto, Canada
Duration: 3 Aug 20257 Aug 2025

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2
ISSN (Print)2154-817X

Conference

Conference31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025
Country/TerritoryCanada
CityToronto
Period3/08/257/08/25

Keywords

  • Approximate algorithm
  • Data mining
  • Data streams
  • Sketch

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