The　popularity　of　the　Internet　of　Things　(IoT)　has　enabled　a　large　number　of　vulnerable　devices　to　connect　to　the　Internet,　bringing　huge　security　risks.　As　a　network-level　security　authentication　method,　device　fingerprint　based　on　machine　learning　has　attracted　considerable　attention　because　it　can　detect　vulnerable　devices　in　complex　and　heterogeneous　access　phases.　However,　flexible　and　diversified　IoT　devices　with　limited　resources　increase　difficulty　of　the　device　fingerprint　authentication　method　executed　in　IoT,　because　it　needs　to　retrain　the　model　network　to　deal　with　incremental　features　or　types.　To　address　this　problem,　a　device　fingerprinting　mechanism　based　on　a　Broad　Learning　System　(BLS)　is　proposed　in　this　paper.　The　mechanism　firstly　characterizes　IoT　devices　by　traffic　analysis　based　on　the　identifiable　differences　of　the　traffic　data　of　IoT　devices,　and　extracts　feature　parameters　of　the　traffic　packets.　A　hierarchical　hybrid　sampling　method　is　designed　at　the　preprocessing　phase　to　improve　the　imbalanced　data　distribution　and　reconstruct　the　fingerprint　dataset.　The　complexity　of　the　dataset　is　reduced　using　Principal　Component　Analysis　(PCA)　and　the　device　type　is　identified　by　training　weights　using　BLS.　The　experimental　results　show　that　the　proposed　method　can　achieve　state-of-the-art　accuracy　and　spend　less　training　time　than　other　existing　methods.