With　the　development　of　the　Internet　of　Things,　smart　devices　are　widely　used.　Hardware　security　is　one　key　issue　in　the　security　of　the　Internet　of　Things.　As　the　core　component　of　the　hardware,　the　integrated　circuit　must　be　taken　seriously　with　its　security.　The　pre-silicon　detection　methods　do　not　require　gold　chips,　are　not　affected　by　process　noise,　and　are　suitable　for　the　safe　detection　of　a　very　large-scale　integration.　Therefore,　more　and　more　researchers　are　paying　attention　to　the　pre-silicon　detection　method.　In　this　study,　we　propose　a　machine-learning-based　hardware-Trojan　detection　method　at　the　gate　level.　First,　we　put　forward　new　Trojan-net　features.　After　that,　we　use　the　scoring　mechanism　of　the　eXtreme　Gradient　Boosting　to　set　up　a　new　effective　feature　set　of　49　out　of　56　features.　Finally,　the　hardware-Trojan　classifier　was　trained　and　detected　based　on　the　new　feature　set　by　the　eXtreme　Gradient　Boosting　algorithm,　respectively.　The　experimental　results　show　that　the　proposed　method　can　obtain　89.84%　average　Recall,　86.75%　average　F-measure,　and　99.83%　average　Accuracy,　which　is　the　best　detection　result　among　existing　machine-learning-based　hardware-Trojan　detection　methods.　©　The　Author(s)　2019.