{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:15:33Z","timestamp":1761581733699,"version":"3.41.0"},"publisher-location":"New York, New York, USA","reference-count":28,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation grants","award":["CNS 1616317, CNS 1616273"],"award-info":[{"award-number":["CNS 1616317, CNS 1616273"]}]},{"name":"NSFC","award":["61672061"],"award-info":[{"award-number":["61672061"]}]},{"name":"National Basic Research Program of China","award":["973 Program, 2014CB340405"],"award-info":[{"award-number":["973 Program, 2014CB340405"]}]},{"name":"Primary Research & Development Plan of China","award":["2016YFB1000304"],"award-info":[{"award-number":["2016YFB1000304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1145\/3229543.3229545","type":"proceedings-article","created":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T19:07:07Z","timestamp":1533150427000},"page":"15-20","source":"Crossref","is-referenced-by-count":25,"title":["Empowering Sketches with Machine Learning for Network Measurements"],"prefix":"10.1145","author":[{"given":"Tong","family":"Yang","sequence":"first","affiliation":[{"name":"Peking University"}]},{"given":"Lun","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University"}]},{"given":"Yulong","family":"Shen","sequence":"additional","affiliation":[{"name":"Xidian University"}]},{"given":"Muhammad","family":"Shahzad","sequence":"additional","affiliation":[{"name":"North Carolina State University"}]},{"given":"Qun","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology at Chinese Academy of Sciences"}]},{"given":"Xiaohong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Future University Hakodate"}]},{"given":"Kun","family":"Tan","sequence":"additional","affiliation":[{"name":"HUAWEI Technologies, Co. LTD"}]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[{"name":"Peking University"}]}],"member":"320","reference":[{"key":"key-10.1145\/3229543.3229545-1","unstructured":"Caida. http:\/\/www.caida.org\/home\/."},{"key":"key-10.1145\/3229543.3229545-2","unstructured":"DPDK websit. http:\/\/dpdk.org\/."},{"key":"key-10.1145\/3229543.3229545-3","doi-asserted-by":"crossref","unstructured":"R. Ben Basat, G. Einziger, R. Friedman, M. C. Luizelli, and E. Waisbard. Constant time updates in hierarchical heavy hitters. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication, pages 127--140. ACM, 2017.","DOI":"10.1145\/3098822.3098832"},{"key":"key-10.1145\/3229543.3229545-4","doi-asserted-by":"crossref","unstructured":"C. Callegari and N. Cyprus. Statistical approaches for network anomaly detection. In Proc. ICIMP, 2009.","DOI":"10.1002\/sec.104"},{"key":"key-10.1145\/3229543.3229545-5","unstructured":"G. Cormode. Sketch techniques for approximate query processing. Foundations and Trends in Databases. NOW publishers, 2011."},{"key":"key-10.1145\/3229543.3229545-6","doi-asserted-by":"crossref","unstructured":"G. Cormode and S. Muthukrishnan. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1):58--75, 2005.","DOI":"10.1016\/j.jalgor.2003.12.001"},{"key":"key-10.1145\/3229543.3229545-7","doi-asserted-by":"crossref","unstructured":"G. Cormode and S. Muthukrishnan. Summarizing and mining skewed data streams. In Proc. SDM, 2005.","DOI":"10.1137\/1.9781611972757.5"},{"key":"key-10.1145\/3229543.3229545-8","doi-asserted-by":"crossref","unstructured":"H. Dai, L. Meng, and A. X. Liu. Finding persistent items in distributed, datasets. In Proc. IEEE INFOCOM, 2018.","DOI":"10.1109\/INFOCOM.2018.8486425"},{"key":"key-10.1145\/3229543.3229545-9","doi-asserted-by":"crossref","unstructured":"H. Dai, M. Shahzad, A. X. Liu, and Y. Zhong. Finding persistent items in data streams. Proceedings of the VLDB Endowment, 10(4):289--300, 2016.","DOI":"10.14778\/3025111.3025112"},{"key":"key-10.1145\/3229543.3229545-10","doi-asserted-by":"crossref","unstructured":"H. Dai, Y. Zhong, A. X. Liu, W. Wang, and M. Li. Noisy bloom filters for multi-set membership testing. In Proc. ACM SIGMETRICS, pages 139--151, 2016.","DOI":"10.1145\/2896377.2901451"},{"key":"key-10.1145\/3229543.3229545-11","doi-asserted-by":"crossref","unstructured":"C. Estan and G. Varghese. New directions in traffic measurement and accounting. ACM SIGMCOMM, 2002.","DOI":"10.1145\/633025.633056"},{"key":"key-10.1145\/3229543.3229545-12","doi-asserted-by":"crossref","unstructured":"S. K. Fayazbakhsh, L. Chiang, V. Sekar, M. Yu, and J. C. Mogul. Enforcing network-wide policies in the presence of dynamic middlebox actions using flowtags. In NSDI, volume 14, pages 533--546, 2014.","DOI":"10.1145\/2491185.2491203"},{"key":"key-10.1145\/3229543.3229545-13","doi-asserted-by":"crossref","unstructured":"P. Flajolet and G. N. Martin. Probabilistic counting algorithms for data base applications. Journal of computer and system sciences, 31(2):182--209, 1985.","DOI":"10.1016\/0022-0000(85)90041-8"},{"key":"key-10.1145\/3229543.3229545-14","unstructured":"S. Haykin and N. Network. A comprehensive foundation. Neural networks. 2(2004):41, 2004."},{"key":"key-10.1145\/3229543.3229545-15","doi-asserted-by":"crossref","unstructured":"M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf. Support vector machines. IEEE Intelligent Systems and their applications, 13(4):18--28, 1998.","DOI":"10.1109\/5254.708428"},{"key":"key-10.1145\/3229543.3229545-16","doi-asserted-by":"crossref","unstructured":"Q. Huang, X. Jin, P. P. Lee, R. Li, L. Tang, Y.-C. Chen, and G. Zhang. Sketchvisor: Robust network measurement for software packet processing. In SIGCOMM 2017.","DOI":"10.1145\/3098822.3098831"},{"key":"key-10.1145\/3229543.3229545-17","doi-asserted-by":"crossref","unstructured":"Q. Huang and P. P. Lee. Ld-sketch: A distributed sketching design for accurate and scalable anomaly detection in network data streams. In IEEE INFOCOM 2014.","DOI":"10.1109\/INFOCOM.2014.6848076"},{"key":"key-10.1145\/3229543.3229545-18","doi-asserted-by":"crossref","unstructured":"T. Li, S. Chen, and Y. Ling. Per-flow traffic measurement through randomized counter sharing. ToN, 2012.","DOI":"10.1109\/INFCOM.2011.5934979"},{"key":"key-10.1145\/3229543.3229545-19","doi-asserted-by":"crossref","unstructured":"Z. Liu, A. Manousis, G. Vorsanger, V. Sekar, and V. Braverman. One sketch to rule them all: Rethinking network flow monitoring with univmon. In Proceedings of the 2016 ACM SIGCOMM Conference, pages 101--114. ACM, 2016.","DOI":"10.1145\/2934872.2934906"},{"key":"key-10.1145\/3229543.3229545-20","unstructured":"C. Monsanto, J. Reich, N Foster, J. Rexford, D. Walker, et al. Composing software defined networks. In NSDI, volume 13, pages 1--13, 2013."},{"key":"key-10.1145\/3229543.3229545-21","doi-asserted-by":"crossref","unstructured":"M. Moshref, M. Yu, R. Govindan, and A. Vahdat. Trumpet: Timely and precise triggers in data centers. In Proceedings of the 2016 ACM SIGCOMM Conference. pages 129--143. ACM, 2016.","DOI":"10.1145\/2934872.2934879"},{"key":"key-10.1145\/3229543.3229545-22","doi-asserted-by":"crossref","unstructured":"V. Sivaraman, S. Narayana, O. Rottenstreich, S. Muthukrishnan, and J. Rexford. Heavy-hitter detection entirely in the data plane. In Proceedings of the Symposium on SDN Research, pages 164--176. ACM, 2017.","DOI":"10.1145\/3050220.3063772"},{"key":"key-10.1145\/3229543.3229545-23","doi-asserted-by":"crossref","unstructured":"K. Xie, X. Li, X. Wang, and et al. Fast tensor factorization for accurate internet anomaly detection. IEEE\/ACM Transactions on Networking, 25(6):3794--3807, 2017.","DOI":"10.1109\/TNET.2017.2761704"},{"key":"key-10.1145\/3229543.3229545-24","doi-asserted-by":"crossref","unstructured":"K. Xie, X. Li, X. Wang, and et al. On-line anomaly detection with high accuracy IEEE\/ACM Transactions on Networking, 2018.","DOI":"10.1109\/TNET.2018.2819507"},{"key":"key-10.1145\/3229543.3229545-25","unstructured":"K. Xie, L. Wang, and et al. Accurate recovery of internet traffic data: A sequential tensor completion approach. IEEE\/ACM Transactions on Networking (TON). 26(2):793--806, 2018."},{"key":"key-10.1145\/3229543.3229545-26","unstructured":"Y. Xie, V. Sekar, D. A. Maltz, M. K. Reiter, and H. Zhang. Worm origin identification using random moonwalks. In Proc. IEEE SPSR, 2005."},{"key":"key-10.1145\/3229543.3229545-27","unstructured":"M. Yu, L. Jose, and R. Miao. Software defined traffic measurement with opensketch. In Proc. USENIX NSDI, pages 29--42, 2013."},{"key":"key-10.1145\/3229543.3229545-28","doi-asserted-by":"crossref","unstructured":"Y. Zhang. An adaptive flow counting method for anomaly detection in sdn. In Proceedings of the ninth ACM conference on Emerging networking experiments and technologies, pages 25--30. ACM, 2013.","DOI":"10.1145\/2535372.2535411"}],"event":{"name":"the 2018 Workshop","start":{"date-parts":[[2018,8,24]]},"sponsor":["SIGCOMM, ACM Special Interest Group on Data Communication"],"location":"Budapest, Hungary","end":{"date-parts":[[2018,8,24]]},"acronym":"NetAI'18"},"container-title":["Proceedings of the 2018 Workshop on Network Meets AI & ML - NetAI'18"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3229543.3229545","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3229545&ftid=1992142&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:39:43Z","timestamp":1750210783000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3229543.3229545"}},"subtitle":[],"proceedings-subject":"Network Meets AI & ML","short-title":[],"issued":{"date-parts":[[2018]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1145\/3229543.3229545","relation":{},"subject":[],"published":{"date-parts":[[2018]]}}}