乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T05:18:37Z","timestamp":1672636717924},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2019,1,28]],"date-time":"2019-01-28T00:00:00Z","timestamp":1548633600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/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":[[2019,1,28]]},"abstract":"We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet. In contrast, DIP computes low-dimensional representations of nodes that preserve structural properties and non-linear relationships across multiple, heterogeneous sources of structural information, such as IP, routing, and distance information. Using a large real-world data set, we show that DIP learns representations that preserve the real-world clustering of the associated nodes and predicts distance between them more than 30% better than a meanbased approach. Furthermore, DIP accurately imputes hop count distance to unknown hosts (i.e., not used in training) given only their IP addresses and routable prefixes. Our framework is extensible to new data sources and applicable to a wide range of problems in network monitoring and security.<\/jats:p>","DOI":"10.1145\/3310165.3310172","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T13:16:22Z","timestamp":1548767782000},"page":"48-54","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning IP network representations"],"prefix":"10.1145","volume":"48","author":[{"given":"Mingda","family":"Li","sequence":"first","affiliation":[{"name":"University of California"}]},{"given":"Bo","family":"Zong","sequence":"additional","affiliation":[{"name":"NEC Laboratories America"}]},{"given":"Cristian","family":"Lumezanu","sequence":"additional","affiliation":[{"name":"NEC Laboratories America"}]},{"given":"Haifeng","family":"Chen","sequence":"additional","affiliation":[{"name":"NEC Laboratories America"}]}],"member":"320","published-online":{"date-parts":[[2019,1,28]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Ark {n. d.}. Ark IPv4 Routed Topology Dataset. http:\/\/www.caida.org\/data\/active\/ipv4_routed_24_topology_dataset.xml. ({n. d.}). Ark {n. d.}. Ark IPv4 Routed Topology Dataset. http:\/\/www.caida.org\/data\/active\/ipv4_routed_24_topology_dataset.xml. ({n. d.})."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(99)00091-4"},{"key":"e_1_2_1_3_1","volume-title":"PIC: Practical Internet Coordinates for Distance Estimation. In ICDCS.","author":"Costa Manuel","year":"2004","unstructured":"Manuel Costa , Miguel Castro , Antony Rowstron , and Peter Key . 2004 . PIC: Practical Internet Coordinates for Distance Estimation. In ICDCS. Manuel Costa, Miguel Castro, Antony Rowstron, and Peter Key. 2004. PIC: Practical Internet Coordinates for Distance Estimation. In ICDCS."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1030194.1015471"},{"key":"e_1_2_1_5_1","volume-title":"Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research","author":"Erhan Dumitru","year":"2010","unstructured":"Dumitru Erhan , Yoshua Bengio , Aaron Courville , Pierre-Antoine Manzagol , Pascal Vincent , and Samy Bengio . 2010. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research ( 2010 ). Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. 2010. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research (2010)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1402958.1402992"},{"key":"e_1_2_1_7_1","volume-title":"Estimating Hop Distance Between Arbitrary Host Pairs","author":"Eriksson Brian","unstructured":"Brian Eriksson , Paul Barford , and Robert Nowak . 2009. Estimating Hop Distance Between Arbitrary Host Pairs . In IEEE Infocom . Brian Eriksson, Paul Barford, and Robert Nowak. 2009. Estimating Hop Distance Between Arbitrary Host Pairs. In IEEE Infocom."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1298306.1298335"},{"key":"e_1_2_1_9_1","volume-title":"arXiv preprint arXiv:1710.11583","author":"Feamster Nick","year":"2017","unstructured":"Nick Feamster and Jennifer Rexford . 2017. Why (and How) Networks Should Run Themselves . arXiv preprint arXiv:1710.11583 ( 2017 ). Nick Feamster and Jennifer Rexford. 2017. Why (and How) Networks Should Run Themselves. arXiv preprint arXiv:1710.11583 (2017)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/948109.948116"},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Andrej Karpathy and Li Fei-Fei. 2015. Deep visual-semantic alignments for generating image descriptions. In CVPR. Andrej Karpathy and Li Fei-Fei. 2015. Deep visual-semantic alignments for generating image descriptions. In CVPR .","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"e_1_2_1_12_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_1_13_1","volume-title":"Deep learning. nature","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015. Deep learning. nature ( 2015 ). Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature (2015)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131365.3131396"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098824"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Tom\u00e1\u0161 Mikolov Martin Karafi\u00e1t Luk\u00e1\u0161 Burget Jan \u010cernock\u1ef3 and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Interspeech. Tom\u00e1\u0161 Mikolov Martin Karafi\u00e1t Luk\u00e1\u0161 Burget Jan \u010cernock\u1ef3 and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Interspeech .","DOI":"10.21437\/Interspeech.2010-343"},{"key":"e_1_2_1_17_1","volume-title":"Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov , Quoc V Le , and Ilya Sutskever . 2013. Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 ( 2013 ). Tomas Mikolov, Quoc V Le, and Ilya Sutskever. 2013. Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013)."},{"key":"e_1_2_1_18_1","unstructured":"T. S. Eugene Ng and Hui Zhang. 2002. Predicting Internet Network Distance with Coordinates-Based Approaches. In INFOCOM. T. S. Eugene Ng and Hui Zhang. 2002. Predicting Internet Network Distance with Coordinates-Based Approaches. In INFOCOM ."},{"key":"e_1_2_1_19_1","unstructured":"Pyxida {n. d.}. Pyxida. http:\/\/pyxida.sourceforge.net\/. ({n. d.}). Pyxida {n. d.}. Pyxida. http:\/\/pyxida.sourceforge.net\/. ({n. d.})."},{"key":"e_1_2_1_20_1","unstructured":"RouteViews. {n. d.}. http:\/\/www.routeviews.org. ({n. d.}). RouteViews. {n. d.}. http:\/\/www.routeviews.org. ({n. d.})."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Neil Spring Ratul Mahajan and Thomas Anderson. 2003. Quantifying the Causes of Path Inflation. In ACM Sigcomm. Neil Spring Ratul Mahajan and Thomas Anderson. 2003. Quantifying the Causes of Path Inflation. In ACM Sigcomm.","DOI":"10.1145\/863955.863970"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_2_1_23_1","volume-title":"Learning two-branch neural networks for image-text matching tasks","author":"Wang Liwei","year":"2018","unstructured":"Liwei Wang , Yin Li , Jing Huang , and Svetlana Lazebnik . 2018. Learning two-branch neural networks for image-text matching tasks . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2018 ). Liwei Wang, Yin Li, Jing Huang, and Svetlana Lazebnik. 2018. Learning two-branch neural networks for image-text matching tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)."},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Xiaohan Zhao Alessandra Sala Haitao Zheng and Ben Y Zhao. 2011. Efficient shortest paths on massive social graphs. In CollaborateCom. Xiaohan Zhao Alessandra Sala Haitao Zheng and Ben Y Zhao. 2011. Efficient shortest paths on massive social graphs. In CollaborateCom .","DOI":"10.4108\/icst.collaboratecom.2011.247162"}],"container-title":["ACM SIGCOMM Computer Communication Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3310165.3310172","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T10:18:32Z","timestamp":1672568312000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3310165.3310172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,28]]},"references-count":24,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,1,28]]}},"alternative-id":["10.1145\/3310165.3310172"],"URL":"https:\/\/doi.org\/10.1145\/3310165.3310172","relation":{},"ISSN":["0146-4833"],"issn-type":[{"value":"0146-4833","type":"print"}],"subject":[],"published":{"date-parts":[[2019,1,28]]},"assertion":[{"value":"2019-01-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}