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Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (<jats:italic>DAD<\/jats:italic>), specifically focusing on <jats:italic>Transmission Control Protocol SYN flood attacks<\/jats:italic>. We compare two different <jats:italic>DAD<\/jats:italic> architectures, called <jats:italic>Standalone<\/jats:italic> and <jats:italic>Correlated DAD<\/jats:italic>, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time <jats:italic>DAD<\/jats:italic>. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\upmu \\text {s}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>\u03bc<\/mml:mi>\n                      <mml:mtext>s<\/mml:mtext>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in the worst case. Considering real-time <jats:italic>DAD<\/jats:italic> implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphic Abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1007\/s10922-021-09633-5","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T17:51:20Z","timestamp":1635875480000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3617-5916","authenticated-orcid":false,"given":"Francesco","family":"Musumeci","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Can","family":"Fidanci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Paolucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filippo","family":"Cugini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"Tornatore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"issue":"1","key":"9633_CR1","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/JPROC.2014.2371999","volume":"103","author":"D Kreutz","year":"2015","unstructured":"Kreutz, D., Ramos, F.M.V., Verissimo, P.E., Rothenberg, C.E., Azodolmolky, S., Uhlig, S.: Software-defined networking: a comprehensive survey. 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