<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things</dc:title><dc:creator>Abdelmoumin, Ghada; Rawat, Danda B.; Rahman, Abdul</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher/><dc:date>2022-03-15</dc:date><dc:nsf_par_id>10337354</dc:nsf_par_id><dc:journal_name>IEEE Internet of Things Journal</dc:journal_name><dc:journal_volume>9</dc:journal_volume><dc:journal_issue>6</dc:journal_issue><dc:page_range_or_elocation>4280 to 4290</dc:page_range_or_elocation><dc:issn>2372-2541</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1109/JIOT.2021.3103829</dc:doi><dcq:identifierAwardId>1828811; 2039583</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>