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
摘要
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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- On-Off Sketch: A Fast and Accurate Sketch on PersistenceYinda Zhang, Jinyang Li, Yutian Lei, Tong Yang 等VLDB 2021 · 被引用 63 次
- BurstSketch: Finding Bursts in Data StreamsZheng Zhong, Shen Yan, Zikun Li, Decheng Tan 等SIGMOD 2021 · 被引用 58 次
- DHS: Adaptive Memory Layout Organization of Sketch Slots for Fast and Accurate Data Stream ProcessingBohan Zhao, Xiang Li, Boyu Tian, Zhiyu Mei 等KDD 2021 · 被引用 47 次
- BitMatcher: Bit-level Counter Adjustment for SketchesQilong Shi, Chengjun Jia, Wenjun Li, Zaoxing Liu 等ICDE 2024 · 被引用 22 次
- Harry: A Scalable SIMD-based Multi-literal Pattern Matching Engine for Deep Packet InspectionHao Xu, Harry Chang, Wenjun Zhu, Yang Hong 等INFOCOM 2023 · 被引用 10 次
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