Computer Science ›› 2026, Vol. 53 ›› Issue (8): 403-412.doi: 10.11896/jsjkx.251100114
• Computer Software • Previous Articles Next Articles
SHU Yapeng1, DU Yang1, HUANG He1, SUN Yu’e2
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| [1] CHEN X,XIAO Q,LIU H,et al.Eagle:Toward scalable andnear-optimal network-wide sketch deployment in network mea-surement[C]//Proceedings of ACM SIGCOMM.2024:291-310. [2] GAO G,ZHOU S,SUN Y,et al.Priority-aware Per-flow Size Measurement in High-speed Networks[J].Journal of Electro-nics & Information Technology,2025,47(6):1885-1895. [3] SUN H,HUANG Q,SUN J,et al.AutoSketch:AutomaticSketch-Oriented compiler for query-driven network telemetry[C]//Proceedings USENIX NSDI.2024:1551-1572. [4] LU J,ZHANG P,LI Z,et al.An Accurate Flow Size Measurement Framework Based on MachineLearning[C]//2025 International Conference on Sensor-Cloud and Edge Computing System(SCECS).IEEE,2025:297-305. [5] GU L,TIAN Y,CHEN W,et al.Per-flow network measurement with distributed sketch[J].IEEE/ACM Transactions on Networking,2023,32(1):411-426. [6] CORMODE G,MUTHUKRISHNAN S.An improved datastream summary:the count-min sketch and its applications[J].Journal of Algorithms,2005,55(1):58-75. [7] CHEN M,CHEN S,CAI Z.Counter tree:A scalable counter architecture for per-flow traffic measurement[J].IEEE/ACM Transactions on Networking,2016,25(2):1249-1262. [8] YANG T,ZHOU Y,JIN H,et al.Pyramid sketch:A sketch framework for frequency estimation of data streams[J].Proceedings VLDB Endowment,2017,10(11):1442-1453. [9] YANG K,LONG S,SHI Q,et al.SketchINT:Empowering INT with TowerSketch for per-flow per-switch measurement[J].IEEE Transactions on Parallel and Distributed Systems,2023,34(11):2876-2894. [10] GAO G,QIAN Z,HUANG H,et al.Tailoredsketch:A fast and adaptive sketch for efficient per-flow size measurement[J].IEEE Transactions on Network Science and Engineering,2024,12(1):505-517. [11] HUANG H,LOU C,SUN Y E,et al.Adaptive denoising for network traffic measurement[J].IEEE Transactions on Network Science and Engineering,2025,12(4):2907-2920. [12] SCHWELLER R,LI Z,CHEN Y,et al.Reversible sketches:enabling monitoring and analysis over high-speed data streams[J].IEEE/ACM Transactions on Networking,2007,15(5):1059-1072. [13] SCHWELLER R,GUPTA A,PARSONS E,et al.Reversiblesketches for efficient and accurate change detection over network data streams[C]//Proceedings of ACM SIGCOMM.2004:207-212. [14] JING X,ZHAO J,ZHENG Q,et al.A reversible sketch-based method for detecting and mitigating amplification attacks[J].Journal of Network and Computer Applications,2019,142:15-24. [15] JING X,YAN Z,JIANG X,et al.Network traffic fusion andanalysis against DDoS flooding attacks with a novel reversible sketch[J].Information Fusion,2019,51:100-113. [16] JING X,HAN H,YAN Z,et al.SuperSketch:A multi-dimen-sional reversible data structure for super host identification[J].IEEE Transactions on Dependable and Secure Computing,2021,19(4):2741-2754. [17] JING X,CAO Q,YAN Z,et al.LocalSketch:An Accurate and Efficient Sketch for Range Spread Estimation[J].IEEE Transactions on Dependable and Secure Computing,2025,22(6):6361-6375. [18] KAUR S,KUMAR K,AGGARWAL N.A review on P4-Programmable data planes:Architecture,research efforts,and future directions[J].Computer Communications,2021,170:109-129. [19] HAN H,YAN Z,JING X,et al.Applications of sketches in network traffic measurement:A survey[J].Information Fusion,2022,82:58-85. [20] ZHOU H,GU G.Securing Networks with Programmable Data Planes:Opportunities and Challenges[J].IEEE Security & Privacy,2025,23(6):42-50. [21] DAS R,SNOEREN A C.Memory management inActiveRMT:Towards runtime-programmable switches[C]//Proceedings of ACM SIGCOMM.2023:1043-1059. [22] YANG Y,HE L,ZHOU J,et al.P4runpro:Enabling runtimeprogrammability forrmt programmable switches[C]//Procee-dings of ACM SIGCOMM.2024:921-937. [23] ZHENG H,TIAN C,YANG T,et al.Flymon:enabling on-the-fly task reconfiguration for network measurement[C]//Proceedings of ACM SIGCOMM.2022:486-502. [24] XI Z,ZHOU Y,ZHANG D,et al.Newton:Intent-driven network traffic monitoring[J].IEEE/ACM Transactions on Networking,2021,30(2):939-952. [25] GAO Y,WANG Z.A review of P4 programmable data planes for network security[J].Mobile Information Systems,2021,2021(1):1257046. [26] WANG P,GUAN X,QIN T,et al.A data streaming method for monitoring host connection degrees of high-speed links[J].IEEE Transactions on Information Forensics and Security,2011,6(3):1086-1098. [27] XU Z,TIAN Y,WU Y,et al.Hidden Sketch:A Space-Efficient Reversible Sketch for Tracking Frequent Items in Data Streams[J].arXiv:2505.12293,2025. [28] WANG F,TANG Y,GAO L,et al.BC-Sketch:A simple reversi-ble sketch for detecting network anomalies[C]//2020 IEEE International Conference on Smart Data Services(SMDS).IEEE,2020:36-44. [29] SUN H,WANG X,BUYYA R,et al.CloudEyes:Cloud-based malware detection with reversible sketch for resource-constrained internet of things(IoT) devices[J].Software:Practice and Experience,2017,47(3):421-441. [30] ZHOU A,QIAN J.Identifying Abnormal Hosts in Data Streams Using Reversible Sketch[J].Engineering Reports,2025,7(5):e70193. [31] LI Z,GENG Y.Score Sketch:An Efficient and ReversibleSketch for Advanced Persistent Threat Detection[C]//2024 5th International Conference on Computers and Artificial Intelligence Technology(CAIT).IEEE,2024:314-319. [32] BASAT R B,EINZIGER G,FRIEDMAN R,et al.Optimal elephant flow detection[C]//IEEE Conference on Computer Communications(IEEE INFOCOM).2017:1-9. [33] ZHENG H,YAN X,LI W,et al.When P4 meets run-to-completion architecture[C]//Proceedings USENIX NSDI.2025:1487-1505. [34] CAIDA.Anonymized internet traces 2019[EB/OL].[2026-06-06].https://catalog.caida.org/details/dataset/passive_2019_pcap. [35] MAWI.MAWI Working Group Traffic Archive[EB/OL].[2026-06-06].https://mawi.wide.ad.jp/mawi/samplepoint-F/2024/. |
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