Pub Date : 1900-01-01DOI: 10.1109/NCA53618.2021.9685554
D. Dobrev, D. Avresky
{"title":"DDoS attack identification based on SDN","authors":"D. Dobrev, D. Avresky","doi":"10.1109/NCA53618.2021.9685554","DOIUrl":"https://doi.org/10.1109/NCA53618.2021.9685554","url":null,"abstract":"","PeriodicalId":114675,"journal":{"name":"IEEE International Symposium on Network Computing and Applications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122201810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"First","authors":"Michael D. Kelly, S. Geoghegan","doi":"10.1109/NCA.2009.44","DOIUrl":"https://doi.org/10.1109/NCA.2009.44","url":null,"abstract":"Table 1. Rental and Homeowner Vacancy Rates for the United States: 2018 to 2023 Rental Vacancy Rate (percent) Homeowner Vacancy Rate (percent) Year First Quarter Second Quarter Third Quarter Fourth Quarter First Quarter Second Quarter Third Quarter Fourth Quarter 2023...... 2022...... 2021...... 6.4 5.8 6.8 5.6 6.2 6.0 5.8 5.8 5.6 0.8 0.8 0.9 0.8 0.9 0.9 0.9 0.8 0.9 2020...... 6.6 5.7 6.4 6.5 1.1 0.9 0.9 1.","PeriodicalId":114675,"journal":{"name":"IEEE International Symposium on Network Computing and Applications","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122579329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. S. Silva, C. C. Machado, R. V. Bisol, L. Granville, A. E. S. Filho
Software-Defined Networking (SDN) aims to alleviate the limitations imposed by traditional IP networks by decoupling network tasks performed on each device in particular planes. This approach offers several benefits, such as standard communication protocols, centralized network functions, and specific network elements, for example, controller devices. Despite these benefits, there is still a lack of adequate support for performing tasks related to traffic classification, because (i) there are traffic profiles that are very similar, which makes their classification difficult (e.g., Both HTTP and DNS flows are characterized by packet bursts), (ii) Open Flow, the key SDN implementation today, only offers native flow features, such as packet and byte count, that do not describe intrinsic traffic profiles, and (iii) there is a lack of support to determine what is the optimal set of flow features to characterize different types of traffic profiles. In this paper, we introduce an architecture to collect, extend, and select flow features for traffic classification in Open Flow-based networks. The main goal of our solution is to offer an extensive set of flow features that can be analyzed and refined and to be capable of finding the optimal subset of features to classify different types of traffic flows. The experimental evaluation of our proposal shows that some features emerge as meaningful, occupying the top positions for the classification of distinct flows in different experimental scenarios.
{"title":"Identification and Selection of Flow Features for Accurate Traffic Classification in SDN","authors":"A. S. Silva, C. C. Machado, R. V. Bisol, L. Granville, A. E. S. Filho","doi":"10.1109/NCA.2015.12","DOIUrl":"https://doi.org/10.1109/NCA.2015.12","url":null,"abstract":"Software-Defined Networking (SDN) aims to alleviate the limitations imposed by traditional IP networks by decoupling network tasks performed on each device in particular planes. This approach offers several benefits, such as standard communication protocols, centralized network functions, and specific network elements, for example, controller devices. Despite these benefits, there is still a lack of adequate support for performing tasks related to traffic classification, because (i) there are traffic profiles that are very similar, which makes their classification difficult (e.g., Both HTTP and DNS flows are characterized by packet bursts), (ii) Open Flow, the key SDN implementation today, only offers native flow features, such as packet and byte count, that do not describe intrinsic traffic profiles, and (iii) there is a lack of support to determine what is the optimal set of flow features to characterize different types of traffic profiles. In this paper, we introduce an architecture to collect, extend, and select flow features for traffic classification in Open Flow-based networks. The main goal of our solution is to offer an extensive set of flow features that can be analyzed and refined and to be capable of finding the optimal subset of features to classify different types of traffic flows. The experimental evaluation of our proposal shows that some features emerge as meaningful, occupying the top positions for the classification of distinct flows in different experimental scenarios.","PeriodicalId":114675,"journal":{"name":"IEEE International Symposium on Network Computing and Applications","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116016171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"NetSolve and Its Applications","authors":"J. Dongarra","doi":"10.1109/NCA.2001.10010","DOIUrl":"https://doi.org/10.1109/NCA.2001.10010","url":null,"abstract":"","PeriodicalId":114675,"journal":{"name":"IEEE International Symposium on Network Computing and Applications","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116061826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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