{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:12:42Z","timestamp":1774926762335,"version":"3.50.1"},"reference-count":106,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGCOMM Comput. Commun. Rev."],"published-print":{"date-parts":[[2024,1,30]]},"abstract":"<jats:p>In-network machine learning inference provides high throughput and low latency. It is ideally located within the network, power efficient, and improves applications' performance. Despite its advantages, the bar to in-network machine learning research is high, requiring significant expertise in programmable data planes, in addition to knowledge of machine learning and the application area. Existing solutions are mostly one-time efforts, hard to reproduce, change, or port across platforms. In this paper, we present Planter: a modular and efficient open-source framework for rapid prototyping of in-network machine learning models across a range of platforms and pipeline architectures. By identifying general mapping methodologies for machine learning algorithms, Planter introduces new machine learning mappings and improves existing ones. It provides users with several example use cases and supports different datasets, and was already extended by users to new fields and applications. Our evaluation shows that Planter improves machine learning performance compared with previous model-tailored works, while significantly reducing resource consumption and co-existing with network functionality. Planter-supported algorithms run at line rate on unmodified commodity hardware, providing billions of inference decisions per second.<\/jats:p>","DOI":"10.1145\/3687230.3687232","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T14:54:57Z","timestamp":1722956097000},"page":"2-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Planter: Rapid Prototyping of In-Network Machine Learning Inference"],"prefix":"10.1145","volume":"54","author":[{"given":"Changgang","family":"Zheng","sequence":"first","affiliation":[{"name":"University of Oxford"}]},{"given":"Mingyuan","family":"Zang","sequence":"additional","affiliation":[{"name":"Technical University of Denmark"}]},{"given":"Xinpeng","family":"Hong","sequence":"additional","affiliation":[{"name":"University of Oxford"}]},{"given":"Liam","family":"Perreault","sequence":"additional","affiliation":[{"name":"University of Oxford"}]},{"given":"Riyad","family":"Bensoussane","sequence":"additional","affiliation":[{"name":"University of Oxford"}]},{"given":"Shay","family":"Vargaftik","sequence":"additional","affiliation":[{"name":"VMware Research"}]},{"given":"Yaniv","family":"Ben-Itzhak","sequence":"additional","affiliation":[{"name":"VMware Research"}]},{"given":"Noa","family":"Zilberman","sequence":"additional","affiliation":[{"name":"University of Oxford"}]}],"member":"320","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n. d.]. Nasdaq ITCH Data Source. ([n. d.]). https:\/\/emi.nasdaq.com\/ITCH\/ Accessed on 02\/05\/2023."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544216.3544263"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387514.3405894"},{"key":"e_1_2_1_4_1","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop Christopher M","unstructured":"Christopher M Bishop and Nasser M Nasrabadi. 2006. Pattern Recognition and Machine Learning. Vol. 4. Springer."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2656877.2656890"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/HCS52781.2021.9567066"},{"key":"e_1_2_1_7_1","volume-title":"pForest: In-Network Inference with Random Forests. CoRR abs\/1909.05680","author":"Busse-Grawitz Coralie","year":"2019","unstructured":"Coralie Busse-Grawitz, Roland Meier, Alexander Dietm\u00fcller, Tobias B\u00fchler, and Laurent Vanbever. 2019. pForest: In-Network Inference with Random Forests. CoRR abs\/1909.05680 (2019). arXiv:1909.05680 http:\/\/arxiv.org\/abs\/1909.05680"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/fut.20334"},{"key":"e_1_2_1_9_1","volume-title":"ICC 2019-2019 IEEE International Conference on Communications (ICC). IEEE, 1--6.","author":"Castanheira Lucas","year":"2019","unstructured":"Lucas Castanheira, Ricardo Parizotto, and Alberto E Schaeffer-Filho. 2019. Flow-Stalker: Comprehensive Traffic Flow Monitoring on the Data Plane using P4. In ICC 2019-2019 IEEE International Conference on Communications (ICC). IEEE, 1--6."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3061609"},{"key":"e_1_2_1_11_1","volume-title":"Filippo Cugini, et al.","author":"Chen Hongyi","year":"2024","unstructured":"Hongyi Chen, Damu Ding, Changgang Zheng, Rana Abu Bakar, Filippo Cugini, et al. 2024. SmartEdge. (2024), 1--57. D4.1 Design of Dynamic & Secure Swarm Networking, GA 101092908."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_13_1","unstructured":"Fran\u00e7ois Chollet et al. 2015. Keras. https:\/\/keras.io. (2015)."},{"key":"e_1_2_1_14_1","first-page":"315","article-title":"A Hybrid Machine Learning System for Stock Market Forecasting","volume":"39","author":"Choudhry Rohit","year":"2008","unstructured":"Rohit Choudhry and Kumkum Garg. 2008. A Hybrid Machine Learning System for Stock Market Forecasting. World Academy of Science, Engineering and Technology 39, 3 (2008), 315--318.","journal-title":"World Academy of Science, Engineering and Technology"},{"key":"e_1_2_1_15_1","volume-title":"https:\/\/github.com\/p4lang\/behavioral-model [accessed","author":"P4 Language Consortium","year":"2022","unstructured":"P4 Language Consortium. 2020. P4 Behavioral-Model. https:\/\/github.com\/p4lang\/behavioral-model [accessed January 26, 2022]. (2020)."},{"key":"e_1_2_1_16_1","volume-title":"Performance of BMv2. https:\/\/github.com\/p4lang\/behavioral-model\/blob\/main\/docs\/performance.md [accessed","author":"P4 Language Consortium","year":"2024","unstructured":"P4 Language Consortium. 2020. Performance of BMv2. https:\/\/github.com\/p4lang\/behavioral-model\/blob\/main\/docs\/performance.md [accessed January 26, 2024]. (2020)."},{"key":"e_1_2_1_17_1","volume-title":"Support-Vector Networks. Machine learning 20, 3","author":"Cortes Corinna","year":"1995","unstructured":"Corinna Cortes and Vladimir Vapnik. 1995. Support-Vector Networks. Machine learning 20, 3 (1995), 273--297."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.2992106"},{"key":"e_1_2_1_19_1","unstructured":"Dell Technologies. 2022. Dell EMC Edge Gateway 5200 Software User's Guide. (2022). https:\/\/www.dell.com\/support\/manuals\/en-ae\/dell-edge-gateway-5200\/egw-5200-software-users-guide"},{"key":"e_1_2_1_20_1","volume-title":"On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine learning 29, 2","author":"Domingos Pedro","year":"1997","unstructured":"Pedro Domingos and Michael Pazzani. 1997. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine learning 29, 2 (1997), 103--130."},{"key":"e_1_2_1_21_1","volume-title":"PCC Vivace: Online-Learning Congestion Control. In 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18)","author":"Dong Mo","year":"2018","unstructured":"Mo Dong, Tong Meng, Doron Zarchy, Engin Arslan, Yossi Gilad, Brighten Godfrey, and Michael Schapira. 2018. PCC Vivace: Online-Learning Congestion Control. In 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18). USENIX Association, Renton, WA, 343--356. https:\/\/www.usenix.org\/conference\/nsdi18\/presentation\/dong"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1936.tb02137.x"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.2307\/1403797"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3482898.3483356"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/14786440109462720"},{"key":"e_1_2_1_28_1","volume-title":"Rui Miao, Yu Zhou, Bingchuan Tian, Chen Sun, Dennis Cai, Ming Zhang, and Minlan Yu.","author":"Gao Jiaqi","year":"2020","unstructured":"Jiaqi Gao, Ennan Zhai, Hongqiang Harry Liu, Rui Miao, Yu Zhou, Bingchuan Tian, Chen Sun, Dennis Cai, Ming Zhang, and Minlan Yu. 2020. Lyra: A Cross-Platform Language and Compiler for Data Plane Programming on Heterogeneous ASICs. In ACM SIGCOMM. 435--450."},{"key":"e_1_2_1_29_1","first-page":"829","article-title":"In-Network Aggregation for Shared Machine Learning Clusters","volume":"3","author":"Gebara Nadeen","year":"2021","unstructured":"Nadeen Gebara, Manya Ghobadi, and Paolo Costa. 2021. In-Network Aggregation for Shared Machine Learning Clusters. Proceedings of Machine Learning and Systems 3 (2021), 829--844.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_2_1_30_1","volume-title":"Jane Street Market Prediction. https:\/\/www.kaggle.com\/c\/jane-street-market-prediction. (2020). [Online","author":"Jane Street Group","year":"2021","unstructured":"Jane Street Group. 2020. Jane Street Market Prediction. https:\/\/www.kaggle.com\/c\/jane-street-market-prediction. (2020). [Online; accessed January 2021]."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3304109.3306226"},{"key":"e_1_2_1_32_1","volume-title":"7th Conference on Innovation in Clouds, Internet and Networks (ICIN).","author":"Hemmatpour Masoud","year":"2024","unstructured":"Masoud Hemmatpour, Changgang Zheng, and Noa Zilberman. 2024. E-Commerce Bot Traffic: In-Network Impact, Detection, and Mitigation. In 7th Conference on Innovation in Clouds, Internet and Networks (ICIN)."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfds.2018.04.003"},{"key":"e_1_2_1_34_1","volume-title":"Proceedings of 3rd international conference on document analysis and recognition","volume":"1","author":"Ho Tin Kam","year":"1995","unstructured":"Tin Kam Ho. 1995. Random Decision Forests. In Proceedings of 3rd international conference on document analysis and recognition, Vol. 1. IEEE, 278--282."},{"key":"e_1_2_1_35_1","volume-title":"Long Short-Term Memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long Short-Term Memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_2_1_36_1","volume-title":"LOBIN: In-Network Machine Learning for Limit Order Books. In 2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR). IEEE, 159--166","author":"Hong Xinpeng","year":"2023","unstructured":"Xinpeng Hong, Changgang Zheng, Stefan Zohren, and Noa Zilberman. 2023. LOBIN: In-Network Machine Learning for Limit Order Books. In 2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR). IEEE, 159--166."},{"key":"e_1_2_1_37_1","volume-title":"Systems, Challenges","author":"Hutter Frank","unstructured":"Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren. 2019. Automated Machine Learning: Methods, Systems, Challenges. Springer Nature."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3225058.3225088"},{"key":"e_1_2_1_39_1","volume-title":"https:\/\/github.com\/barefootnetworks\/Open-Tofino\/blob\/master\/PUBLIC_Tofino-Native-Arch-Document.pdf","author":"Native Intel\u00ae Tofino\u2122","year":"2021","unstructured":"Intel. 2021. P4_16 Intel\u00ae Tofino\u2122 Native Architecture - Public Version. (2021). https:\/\/github.com\/barefootnetworks\/Open-Tofino\/blob\/master\/PUBLIC_Tofino-Native-Arch-Document.pdf"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2011.2162112"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132764"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2890955.2890968"},{"key":"e_1_2_1_43_1","volume-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in neural information processing systems 30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_1_44_1","volume-title":"Muhammad Awais Azam, Amin Karami, Khaled H Alyoubi, and Ahmed S Alfakeeh.","author":"Khan Wasiat","year":"2020","unstructured":"Wasiat Khan, Mustansar Ali Ghazanfar, Muhammad Awais Azam, Amin Karami, Khaled H Alyoubi, and Ahmed S Alfakeeh. 2020. Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing (2020), 1--24."},{"key":"e_1_2_1_45_1","volume-title":"In-Band Network Telemetry (INT). Tech. Spec","author":"Kim Changhoon","year":"2016","unstructured":"Changhoon Kim, Parag Bhide, E Doe, H Holbrook, A Ghanwani, D Daly, M Hira, and B Davie. 2016. In-Band Network Telemetry (INT). Tech. Spec (2016)."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229543.3229550"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477482.3477486"},{"key":"e_1_2_1_48_1","volume-title":"ATP: In-Network Aggregation for Multi-Tenant Learning. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21)","author":"Lao ChonLam","year":"2021","unstructured":"ChonLam Lao, Yanfang Le, Kshiteej Mahajan, Yixi Chen, Wenfei Wu, Aditya Akella, and Michael Swift. 2021. ATP: In-Network Aggregation for Multi-Tenant Learning. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). USENIX Association, 741--761. https:\/\/www.usenix.org\/conference\/nsdi21\/presentation\/lao"},{"key":"e_1_2_1_49_1","volume-title":"High Frequency Trading Acceleration Using FPGAs. In 2011 21st International Conference on Field Programmable Logic and Applications. IEEE, 317--322","author":"Leber Christian","year":"2011","unstructured":"Christian Leber, Benjamin Geib, and Heiner Litz. 2011. High Frequency Trading Acceleration Using FPGAs. In 2011 21st International Conference on Field Programmable Logic and Applications. IEEE, 317--322."},{"key":"e_1_2_1_50_1","volume-title":"SwitchTree: In-Network Computing and Traffic Analyses with Random Forests. Neural Computing and Applications","author":"Lee Jong-Hyouk","year":"2020","unstructured":"Jong-Hyouk Lee and Kamal Singh. 2020. SwitchTree: In-Network Computing and Traffic Analyses with Random Forests. Neural Computing and Applications (2020), 1--12."},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the 18th International Database Engineering & Applications Symposium. 274--277","author":"Kai-Sang Leung Carson","year":"2014","unstructured":"Carson Kai-Sang Leung, Richard Kyle MacKinnon, and Yang Wang. 2014. A Machine Learning Approach for Stock Price Prediction. In Proceedings of the 18th International Database Engineering & Applications Symposium. 274--277."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.09.055"},{"key":"e_1_2_1_53_1","volume-title":"Kai Ming Ting, and Zhi-Hua Zhou","author":"Liu Fei Tony","year":"2008","unstructured":"Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation Forest. In 2008 eighth ieee international conference on data mining. IEEE, 413--422."},{"key":"e_1_2_1_54_1","volume-title":"A Low-Latency Library in FPGA Hardware for High-Frequency Trading (HFT). In 2012 IEEE 20th annual symposium on high-performance interconnects","author":"Lockwood John W","unstructured":"John W Lockwood, Adwait Gupte, Nishit Mehta, Michaela Blott, Tom English, and Kees Vissers. 2012. A Low-Latency Library in FPGA Hardware for High-Frequency Trading (HFT). In 2012 IEEE 20th annual symposium on high-performance interconnects. IEEE, 9--16."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3482898.3483358"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341302.3342080"},{"key":"e_1_2_1_57_1","volume-title":"UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection systems (UNSW-NB15 Network Data Set). In 2015 military communications and information systems conference (MilCIS)","author":"Moustafa Nour","unstructured":"Nour Moustafa and Jill Slay. 2015. UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection systems (UNSW-NB15 Network Data Set). In 2015 military communications and information systems conference (MilCIS). IEEE, 1--6."},{"key":"e_1_2_1_58_1","unstructured":"NASDAQ OMX PSX. 2014. NASDAQ OMX PSX TotalView-ITCH 5.0. http:\/\/www.nasdaqtrader.com\/content\/technicalsupport\/specifications\/dataproducts\/PSXTVITCHSpecification_5.0.pdf. (2014)."},{"key":"e_1_2_1_59_1","unstructured":"Vincent Natoli. 2010. Kudos for CUDA. https:\/\/www.hpcwire.com\/2010\/07\/06\/kudos_for_cuda\/. (2010)."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.2307\/2344614"},{"key":"e_1_2_1_61_1","volume-title":"Neural Networks and Deep Learning","author":"Nielsen Michael A","unstructured":"Michael A Nielsen. 2015. Neural Networks and Deep Learning. Vol. 25. Determination press San Francisco, CA, USA."},{"key":"e_1_2_1_62_1","volume-title":"https:\/\/p4.org\/p4-spec\/docs\/PSA-v1.1.0.html","author":"The P4.org Architecture Working Group","year":"2017","unstructured":"The P4.org Architecture Working Group. 2017. P4_16 PSA Specification (v1.1). (2017). https:\/\/p4.org\/p4-spec\/docs\/PSA-v1.1.0.html"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1364\/JOCN.11.000A84"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.021.1900599"},{"key":"e_1_2_1_65_1","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga et al. 2019. PyTorch: An Imperative Style High-Performance Deep Learning Library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_2_1_66_1","volume-title":"Scikit-learn: Machine Learning in Python. the Journal of machine Learning research 12","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine Learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830."},{"key":"e_1_2_1_67_1","unstructured":"Larry Peterson Carmelo Cascone Brian O'Connor Thomas Vachuska and Bruce Davie. 2021. Software-Defined Networks: A Systems Approach. Systems Approach LLC. https:\/\/sdn.systemsapproach.org"},{"key":"e_1_2_1_68_1","volume-title":"Anna Veronika Dorogush, and Andrey Gulin","author":"Prokhorenkova Liudmila","year":"2018","unstructured":"Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_2_1_69_1","volume-title":"2020 IFIP Networking Conference (Networking). IEEE, 352--360","author":"Qin Qiaofeng","year":"2020","unstructured":"Qiaofeng Qin, Konstantinos Poularakis, Kin K Leung, and Leandros Tassiulas. 2020. Line-Speed and Scalable Intrusion Detection at the Network Edge via Federated Learning. In 2020 IFIP Networking Conference (Networking). IEEE, 352--360."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/356924.356930"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229591.3229594"},{"key":"e_1_2_1_73_1","unstructured":"Amedeo Sapio Marco Canini Chen-Yu Ho Jacob Nelson Panos Kalnis Changhoon Kim Arvind Krishnamurthy Masoud Moshref Dan Ports and Peter Richtarik. 2021. Scaling Distributed Machine Learning with In-Network Aggregation. In USENIX NSDI. 785--808."},{"key":"e_1_2_1_74_1","first-page":"108","article-title":"Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization","volume":"1","author":"Sharafaldin Iman","year":"2018","unstructured":"Iman Sharafaldin, Arash Habibi Lashkari, and Ali A Ghorbani. 2018. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. ICISSp 1 (2018), 108--116.","journal-title":"ICISSp"},{"key":"e_1_2_1_75_1","volume-title":"Stock Market Forecasting Using Machine Learning Algorithms. Department of Electrical Engineering","author":"Shen Shunrong","year":"2012","unstructured":"Shunrong Shen, Haomiao Jiang, and Tongda Zhang. 2012. Stock Market Forecasting Using Machine Learning Algorithms. Department of Electrical Engineering, Stanford University, Stanford, CA (2012), 1--5."},{"key":"e_1_2_1_76_1","volume-title":"Rearchitecting Traffic Analysis with Neural Network Interface Cards. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)","author":"Siracusano Giuseppe","year":"2022","unstructured":"Giuseppe Siracusano, Salvator Galea, Davide Sanvito, Mohammad Malekzadeh, Gianni Antichi, Paolo Costa, Hamed Haddadi, and Roberto Bifulco. 2022. Rearchitecting Traffic Analysis with Neural Network Interface Cards. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22). 513--533."},{"key":"e_1_2_1_77_1","volume-title":"Proc. Workshop Syst. ML Open Source Softw. NeurIPS.","author":"Siracusano Giuseppe","year":"2018","unstructured":"Giuseppe Siracusano, Davide Sanvito, Salvator Galea, and Roberto Bifulco. 2018. Deep Learning Inference on Commodity Network Interface Cards. In Proc. Workshop Syst. ML Open Source Softw. NeurIPS."},{"key":"e_1_2_1_78_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/biomet\/12.1-2.1","article-title":"On the Standard Deviations of Adjusted and Interpolated Values of an Observed Polynomial Function and its Constants and the Guidance they give Towards a Proper Choice of the Distribution of Observations","volume":"12","author":"Smith Kirstine","year":"1918","unstructured":"Kirstine Smith. 1918. On the Standard Deviations of Adjusted and Interpolated Values of an Observed Polynomial Function and its Constants and the Guidance they give Towards a Proper Choice of the Distribution of Observations. Biometrika 12, 1\/2 (1918), 1--85.","journal-title":"Biometrika"},{"key":"e_1_2_1_79_1","doi-asserted-by":"crossref","unstructured":"Hardik Soni Myriana Rifai Praveen Kumar Ryan Doenges and Nate Foster. 2020. Composing Dataplane Programs with &mu;P4. In ACM SIGCOMM. 329--343.","DOI":"10.1145\/3387514.3405872"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/DISCEX.2000.821515"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503222.3507726"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3582016.3582022"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302424.3303979"},{"key":"e_1_2_1_85_1","volume-title":"2018 IEEE 19th International Conference on High Performance Switching and Routing (HPSR). IEEE, 1--8.","author":"V\u00f6r\u00f6s P\u00e9ter","year":"2018","unstructured":"P\u00e9ter V\u00f6r\u00f6s, D\u00e1niel Horp\u00e1csi, R\u00f3bert Kitlei, D\u00e1niel Lesk\u00f3, M\u00e1t\u00e9 Tejfel, and S\u00e1ndor Laki. 2018. T4p4s: A target-independent compiler for protocol-independent packet processors. In 2018 IEEE 19th International Conference on High Performance Switching and Routing (HPSR). IEEE, 1--8."},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2020.3036497"},{"key":"e_1_2_1_87_1","doi-asserted-by":"crossref","unstructured":"Xindong Wu Vipin Kumar J Ross Quinlan Joydeep Ghosh Qiang Yang Hiroshi Motoda Geoffrey J McLachlan Angus Ng Bing Liu S Yu Philip et al. 2008. Top 10 Algorithms in Data Mining. Knowledge and information systems 14 1 (2008) 1--37.","DOI":"10.1007\/s10115-007-0114-2"},{"key":"e_1_2_1_88_1","volume-title":"Programmable Switches for In-Networking Classification. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications. IEEE, 1--10","author":"Xavier Bruno Missi","year":"2021","unstructured":"Bruno Missi Xavier, Rafael Silva Guimar\u00e3es, Giovanni Comarela, and Magnos Martinello. 2021. Programmable Switches for In-Networking Classification. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications. IEEE, 1--10."},{"key":"e_1_2_1_89_1","unstructured":"Xilinx. Accessed on 04\/15\/2023. Xilinx OpenNIC Shell. https:\/\/github.com\/Xilinx\/open-nic. (Accessed on 04\/15\/2023)."},{"key":"e_1_2_1_90_1","unstructured":"Xilinx. Accessed on 04\/16\/2023. Vitis Networking P4 User Guide. https:\/\/docs.xilinx.com\/r\/en-US\/ug1308-vitis-p4-user-guide\/Target-Architecture. (Accessed on 04\/16\/2023)."},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/3365609.3365864"},{"key":"e_1_2_1_92_1","volume-title":"Towards Continuous Threat Defense: In-Network Traffic Analysis for IoT Gateways","author":"Zang Mingyuan","year":"2023","unstructured":"Mingyuan Zang, Changgang Zheng, Lars Dittmann, and Noa Zilberman. 2023. Towards Continuous Threat Defense: In-Network Traffic Analysis for IoT Gateways. IEEE Internet of Things Journal (2023)."},{"key":"e_1_2_1_93_1","doi-asserted-by":"crossref","unstructured":"Mingyuan Zang Changgang Zheng Tomasz Koziak Noa Zilberman and Lars Dittmann. 2023. Federated Learning-Based In-Network Traffic Analysis on IoT Edge. In Security for IoT Networks and Devices in 6G (Sec4IoT) IFIP Networking.","DOI":"10.23919\/IFIPNetworking57963.2023.10186438"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1145\/3546037.3546060"},{"key":"e_1_2_1_95_1","volume-title":"Fung Po Tso, and Weijia Jia","author":"Zhang Xiaoquan","year":"2021","unstructured":"Xiaoquan Zhang, Lin Cui, Fung Po Tso, and Weijia Jia. 2021. pHeavy: Predicting Heavy Flows in the Programmable Data Plane. IEEE Transactions on Network and Service Management (2021)."},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1049\/cje.2016.11.016"},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2907260"},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3344351"},{"key":"e_1_2_1_99_1","volume-title":"Proceedings of the 2nd ACM SIGCOMM Workshop on Future of Internet Routing & Addressing.","author":"Zheng Changgang","year":"2023","unstructured":"Changgang Zheng, Benjamin Rienecker, and Noa Zilberman. 2023. QCMP: Load Balancing via In-Network Reinforcement Learning. In Proceedings of the 2nd ACM SIGCOMM Workshop on Future of Internet Routing & Addressing."},{"key":"e_1_2_1_100_1","volume-title":"Proceedings of the ACM on Networking 1, CoNEXT3","author":"Zheng Changgang","year":"2023","unstructured":"Changgang Zheng, Haoyue Tang, Mingyuan Zang, Xinpeng Hong, Aosong Feng, Leandros Tassiulas, and Noa Zilberman. 2023. DINC: Toward Distributed In-Network Computing. Proceedings of the ACM on Networking 1, CoNEXT3 (2023), 1--25."},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2024.3364757"},{"key":"e_1_2_1_102_1","unstructured":"Changgang Zheng Mingyuan Zang Xinpeng Hong Riyad Bensoussane Shay Vargaftik Yaniv Ben-Itzhak and Noa Zilberman. 2022. Automating In-Network Machine Learning. (2022). arXiv:cs.NI\/2205.08824"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3472716.3472846"},{"key":"e_1_2_1_104_1","unstructured":"Changgang Zheng et al. 2022. Planter's GitHub Repository. https:\/\/github.com\/In-Network-Machine-Learning\/Planter. (2022)."},{"key":"e_1_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796880"},{"key":"e_1_2_1_106_1","volume-title":"An Efficient Design of Intelligent Network Data Plane. In 32nd USENIX Security Symposium (USENIX Security 23)","author":"Zhou Guangmeng","year":"2023","unstructured":"Guangmeng Zhou, Zhuotao Liu, Chuanpu Fu, Qi Li, and Ke Xu. 2023. An Efficient Design of Intelligent Network Data Plane. In 32nd USENIX Security Symposium (USENIX Security 23). Anaheim, CA: USENIX Association."}],"container-title":["ACM SIGCOMM Computer Communication Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3687230.3687232","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3687230.3687232","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:01Z","timestamp":1750294681000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3687230.3687232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,30]]},"references-count":106,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,30]]}},"alternative-id":["10.1145\/3687230.3687232"],"URL":"https:\/\/doi.org\/10.1145\/3687230.3687232","relation":{},"ISSN":["0146-4833"],"issn-type":[{"value":"0146-4833","type":"print"}],"subject":[],"published":{"date-parts":[[2024,1,30]]},"assertion":[{"value":"2024-08-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}