{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:21:11Z","timestamp":1743826871028,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031879944","type":"print"},{"value":"9783031879951","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-87995-1_7","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T19:03:56Z","timestamp":1743793436000},"page":"106-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FLARE: An FPGA-Based Universal Large Flow Detection Engine"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8197-0097","authenticated-orcid":false,"given":"Arish","family":"Sateesan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4258-2208","authenticated-orcid":false,"given":"Jo","family":"Vliegen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8753-7895","authenticated-orcid":false,"given":"Nele","family":"Mentens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Liu, Z., Manousis, A., Vorsanger, G., Sekar, V., Braverman, V.: One sketch to rule them all: rethinking network flow monitoring with UnivMon. In: Proceedings of ACM Special Interest Group Data Commun. (SIGCOMM), pp. 101\u2013114 (2016)","DOI":"10.1145\/2934872.2934906"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Yang, T., et al.: Elastic sketch: adaptive and fast network-wide measurements. In: Proceedings of ACM Special Interest Group Data Communication (SIGCOMM), pp. 561\u2013575 (2018)","DOI":"10.1145\/3230543.3230544"},{"issue":"5","key":"7_CR3","doi-asserted-by":"publisher","first-page":"2350","DOI":"10.1109\/TNET.2020.3011798","volume":"28","author":"L Tang","year":"2020","unstructured":"Tang, L., Huang, Q., Lee, P.: A fast and compact invertible sketch for network-wide heavy flow detection. IEEE\/ACM Trans. Networking 28(5), 2350\u20132363 (2020)","journal-title":"IEEE\/ACM Trans. Networking"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Wu, H., Hsiao, H.C., Hu, Y.C.: Efficient large flow detection over arbitrary windows: an algorithm exact outside an ambiguity region. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 209\u2013222 (2014)","DOI":"10.1145\/2663716.2663724"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Scherrer, S., et al.: Low-rate overuse flow tracer (loft): an efficient and scalable algorithm for detecting overuse flows. In: 2021 40th International Symposium on Reliable Distributed Systems (SRDS), pp. 265\u2013276. IEEE (2021)","DOI":"10.1109\/SRDS53918.2021.00034"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Scherrer, S., et al.: Albus: a probabilistic monitoring algorithm to counter burst-flood attacks. In: 2023 42th International Symposium on Reliable Distributed Systems (SRDS), IEEE (2023)","DOI":"10.1109\/SRDS60354.2023.00025"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Xiao, Q., Cai, X., Qin, Y., Tang, Z., Chen, S., Liu, Y.: Universal and accurate sketch for estimating heavy hitters and moments in data streams. IEEE\/ACM Trans. Networking (2023)","DOI":"10.1109\/TNET.2022.3216025"},{"key":"7_CR8","unstructured":"Carpet Bomb DDoS attacks: on the rise and evading detection. https:\/\/www.corero.com\/threat-report-carpet-bomb-intro\/ (2023)"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Yang, T., et al.: Sf-sketch: a fast, accurate, and memory efficient data structure to store frequencies of data items. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 103\u2013106 (2017)","DOI":"10.1109\/ICDE.2017.50"},{"issue":"1","key":"7_CR10","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.jalgor.2003.12.001","volume":"55","author":"G Cormode","year":"2005","unstructured":"Cormode, G., Muthukrishnan, S.: An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1), 58\u201375 (2005)","journal-title":"J. Algorithms"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Hoozemans, J., Peltenburg, J., Nonnemacher, F., Hadnagy, A., Al-Ars, Z., Hofstee, H.P.: Fpga acceleration for big data analytics: challenges and opportunities. IEEE Circ. Syst. Mag. 21(2), 30\u201347 (2021)","DOI":"10.1109\/MCAS.2021.3071608"},{"key":"7_CR12","unstructured":"Fu, Y.: Adaptable machine learning with alveo data center acceleration cards. https:\/\/www.xilinx.com\/publications\/events\/machine-learning-live\/colorado\/AdaptableMachineLearning_with_Alveo.pdf (2018)"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Chung, E., et al.: Serving dnns in real time at datacenter scale with project brainwave. iEEE Micro 38(2), 8\u201320 (2018)","DOI":"10.1109\/MM.2018.022071131"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Putnam, A., et al.: A reconfigurable fabric for accelerating large-scale datacenter services. ACM SIGARCH Comput. Archit. News 42(3), 13\u201324 (2014)","DOI":"10.1145\/2678373.2665678"},{"key":"7_CR15","unstructured":"J., Abel, F., Hagleitner, C., Herkersdorf, A.: Enabling FPGAs in hyperscale data centers. In: 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp. 1078\u20131086. IEEE (2015)"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Weerasinghe, J., Polig, R., Abel, F., Hagleitner, C.: Network-attached FPGAs for data center applications. In: 2016 International Conference on Field-Programmable Technology (FPT), pp. 36\u201343. IEEE (2016)","DOI":"10.1109\/FPT.2016.7929186"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Hassan, M.: On the off-chip memory latency of real-time systems: Is ddr dram really the best option? In: 2018 IEEE Real-Time Systems Symposium (RTSS), pp. 495\u2013505. IEEE (2018)","DOI":"10.1109\/RTSS.2018.00062"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Sateesan, A., Vliegen, J., Scherrer, S., Hsiao, H.C., Perrig, A., Mentens, N.: SPArch: a hardware-oriented sketch-based architecture for high-speed network flow measurements. ACM Trans. Privacy Secur. (2024)","DOI":"10.1145\/3687477"},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"104619","DOI":"10.1016\/j.micpro.2022.104619","volume":"93","author":"A Sateesan","year":"2022","unstructured":"Sateesan, A., Vliegen, J., Daemen, J., Mentens, N.: Hardware-oriented optimization of bloom filter algorithms and architectures for ultra-high-speed lookups in network applications. Microprocess. Microsyst. 93, 104619 (2022)","journal-title":"Microprocess. Microsyst."},{"key":"7_CR20","unstructured":"Xilinx. Alveo U250 data center accelerator card. https:\/\/www.xilinx.com\/products\/boards-and-kits\/alveo\/u250.html (2023)"},{"key":"7_CR21","unstructured":"Xilinx. Vitis unified software platform documentation: Application acceleration development (ug1393). https:\/\/docs.xilinx.com\/r\/en-US\/ug1393-vitis-application-acceleration (2023)"},{"key":"7_CR22","unstructured":"Xilinx. XUP Vitis Network Example (VNx). https:\/\/github.com\/Xilinx\/xup_vitis_network_example"},{"issue":"9","key":"7_CR23","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1016\/j.jpdc.2006.04.007","volume":"66","author":"Yu Chen","year":"2006","unstructured":"Chen, Yu., Hwang, K.: Collaborative detection and filtering of shrew ddos attacks using spectral analysis. J. Parall. Distrib. Comput. 66(9), 1137\u20131151 (2006)","journal-title":"J. Parall. Distrib. Comput."},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Pham-Quoc, C., Nguyen, B., Thinh, T.N.: Fpga-based multicore architecture for integrating multiple ddos defense mechanisms. ACM SIGARCH Comput. Archit. News 44(4), 14\u201319 (2017)","DOI":"10.1145\/3039902.3039906"},{"key":"7_CR25","unstructured":"Stop DDoS attacks before they disrupt the customer experience. https:\/\/intel.ly\/2N9hexa (2020)"},{"key":"7_CR26","unstructured":"Liu, Z., et al.: Jaqen: a $$\\{$$High-Performance$$\\}$$$$\\{$$Switch-Native$$\\}$$ approach for detecting and mitigating volumetric $$\\{$$DDoS$$\\}$$ attacks with programmable switches. In: 30th USENIX Security Symposium (USENIX Security 21), pp. 3829\u20133846 (2021)"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, M., et al.: Poseidon: mitigating volumetric ddos attacks with programmable switches. In: The 27th Network and Distributed System Security Symposium (NDSS 2020) (2020)","DOI":"10.14722\/ndss.2020.24007"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhang, Y., Ma, C., Chen, S., Odegbile, O.O.: Generalized sketch families for network traffic measurement. Proc. ACM on Measure. Anal. Comput. Syst. 3(3), 1\u201334 (2019)","DOI":"10.1145\/3366699"},{"key":"7_CR29","doi-asserted-by":"crossref","unstructured":"Ramanathan, S., Mirkovic, J., Yu, M., Zhang, Y.: Senss against volumetric ddos attacks. In: Proceedings of the 34th Annual Computer Security Applications Conference, pp. 266\u2013277 (2018)","DOI":"10.1145\/3274694.3274717"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Oikonomou, G., Mirkovic, J., Reiher, P., Robinson, M.: A framework for a collaborative ddos defense. In: 2006 22nd Annual Computer Security Applications Conference (ACSAC 2006), pp. 33\u201342. IEEE (2006)","DOI":"10.1109\/ACSAC.2006.5"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Lee, S.B., Kang, M.S., Gligor, V.D.: Codef: collaborative defense against large-scale link-flooding attacks. In: Proceedings of the ninth ACM conference on Emerging Networking Experiments and Technologies, pp. 417\u2013428 (2013)","DOI":"10.1145\/2535372.2535398"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"Falsafi, B., Dally, B., Singh, D., Chiou, D., Joshua, J.Y., Sendag, R.: FPGAs versus GPUs in data centers. IEEE Micro 37(1), 60\u201372 (2017)","DOI":"10.1109\/MM.2017.19"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Bobda, C., et al.: The future of fpga acceleration in datacenters and the cloud. ACM Trans. Reconfigurable Technol. Syst. (TRETS) 15(3), 1\u201342 (2022)","DOI":"10.1145\/3506713"},{"key":"7_CR34","unstructured":"Wang, Z., Huang, H., Zhang, J., Wu, F., Alonso, G.: $$\\{$$FpgaNIC$$\\}$$: An $$\\{$$FPGA-based$$\\}$$ versatile 100gb $$\\{$$SmartNIC$$\\}$$ for $$\\{$$GPUs$$\\}$$. In: 2022 USENIX Annual Technical Conference (USENIX ATC 22), pp. 967\u2013986 (2022)"},{"key":"7_CR35","unstructured":"AMD. AMD OpenNIC project. https:\/\/github.com\/Xilinx\/open-nic\/blob\/main\/OpenNIC_manual.pdf. Accessed 2024"},{"key":"7_CR36","doi-asserted-by":"crossref","unstructured":"He, Z., Korolija, D., Alonso, G.: Easynet: 100 gbps network for hls. In: 2021 31st International Conference on Field-Programmable Logic and Applications (FPL), pp. 197\u2013203. IEEE (2021)","DOI":"10.1109\/FPL53798.2021.00040"},{"key":"7_CR37","doi-asserted-by":"crossref","unstructured":"Chiosa, M., Preu\u00dfer, T.B., Alonso, G.: Skt: a one-pass multi-sketch data analytics accelerator. Proc. VLDB Endowment 14(11), 2369\u20132382 (2021)","DOI":"10.14778\/3476249.3476287"},{"key":"7_CR38","doi-asserted-by":"crossref","unstructured":"Jiang, W., Korolija, D., Alonso, G.: Data processing with FPGAs on modern architectures. In: Companion of the 2023 International Conference on Management of Data, pp. 77\u201382 (2023)","DOI":"10.1145\/3555041.3589410"},{"key":"7_CR39","doi-asserted-by":"crossref","unstructured":"Jiang, W., et\u00a0al. Fleetrec: large-scale recommendation inference on hybrid gpu-fpga clusters. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3097\u20133105 (2021)","DOI":"10.1145\/3447548.3467139"},{"key":"7_CR40","doi-asserted-by":"crossref","unstructured":"Bex, L., Turan, F., Van Beirendonck, M., Verbauwhede, I.: Mining cryptonight-haven on the varium c1100 blockchain accelerator card. arXiv preprint arXiv:2212.05033 (2022)","DOI":"10.1109\/FPL57034.2022.00074"}],"container-title":["Lecture Notes in Computer Science","Applied Reconfigurable Computing. Architectures, Tools, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-87995-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T19:04:07Z","timestamp":1743793447000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-87995-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031879944","9783031879951"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-87995-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Applied Reconfigurable Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seville","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"arc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/arc2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}