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Netw."],"published-print":{"date-parts":[[2022,8,31]]},"abstract":"<jats:p>\n            <jats:bold>Software-Defined Networks<\/jats:bold>\n            (\n            <jats:bold>SDNs<\/jats:bold>\n            ), with their segregated data and control planes, has proved to be capable of managing massive amounts of data by leveraging distributed information available across the network for informed decision-making at the network controller. However, with the proliferation of next-generation, real-time\n            <jats:bold>Internet of Things<\/jats:bold>\n            (\n            <jats:bold>IoT<\/jats:bold>\n            ) applications that vary greatly in terms of data frequency and volumes, data traffic classification can substantially assist SDN controllers toward efficient routing and traffic engineering decisions. Existing works on network classification are limited by their application-centric nature, thus overlooking the key criterion for real-time IoT applications, namely,\n            <jats:bold>Quality of Service<\/jats:bold>\n            (\n            <jats:bold>QoS<\/jats:bold>\n            ). In this article, we focus on augmenting SDN controllers\u2019 decision-making capacity and Underwater Sensor Networks with machine learning algorithms to achieve real-time, QoS-aware, network traffic classification. Three classifiers, namely, Feed-forward Neural Network, Na\u00efve Bayes, and Logistics Regression have been employed with a novel Artificial Neural Network and Particle Swarm Optimization hybridization scheme by carrying first- and second-order stability analysis for performance improvement of these classifiers. In short, the proposed framework exploits optimization algorithms and semi-supervised\n            <jats:bold>machine learning<\/jats:bold>\n            (\n            <jats:bold>ML<\/jats:bold>\n            ) for precise traffic classification while keeping communication overhead between controller and switches minimal. Results obtained from real-life datasets demonstrate the efficacy of our proposed scheme.\n          <\/jats:p>","DOI":"10.1145\/3474556","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T21:44:01Z","timestamp":1645652641000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Traffic Classification in Underwater Networks Using SDN and Data-Driven Hybrid Metaheuristics"],"prefix":"10.1145","volume":"18","author":[{"given":"B.","family":"Pradhan","sequence":"first","affiliation":[{"name":"National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Brandon University, Brandon, Manitoba, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D. S.","family":"Roy","sequence":"additional","affiliation":[{"name":"National Institute of Technology Meghalaya, Shillong, Meghalaya, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. H. K.","family":"Reddy","sequence":"additional","affiliation":[{"name":"National Institute of Science and Technology, Visakhapatnam, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-9709","authenticated-orcid":false,"given":"Jerry Chun-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Western Norway University of Applied Sciences, Inndalsveien, Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,4,18]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","volume-title":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","author":"Alshammari Riyad","year":"2009","unstructured":"Riyad Alshammari and A. Nur Zincir-Heywood. 2009. Machine learning based encrypted traffic classification: Identifying SSH and Skype. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. IEEE, 1\u20138."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2016.7785327"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.985692"},{"key":"e_1_3_2_5_2","first-page":"1942","volume-title":"Proceedings of the IEEE International Conference on Neural Networks","volume":"4","author":"Eberhart Russell","year":"1995","unstructured":"Russell Eberhart and James Kennedy. 1995. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, Vol. 4. Citeseer, 1942\u20131948."},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/PHOSST.2017.8012677","volume-title":"2017 IEEE Photonics Society Summer Topical Meeting Series (SUM\u201917)","author":"Glick Madeleine","year":"2017","unstructured":"Madeleine Glick and Houman Rastegarfar. 2017. Scheduling and control in hybrid data centers. 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Parizi, Gautam Srivastava, Ali Dehghantanha, and Kim-Kwang Raymond Choo. 2019. Energy efficient decentralized authentication in internet of underwater things using blockchain. In 2019 IEEE Globecom Workshops (GC Wkshps\u201919). IEEE, 1\u20136."}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474556","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474556","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:50Z","timestamp":1750191530000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,18]]},"references-count":23,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,8,31]]}},"alternative-id":["10.1145\/3474556"],"URL":"https:\/\/doi.org\/10.1145\/3474556","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,18]]},"assertion":[{"value":"2021-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}