Real　open　network　environments　include　the　traffic　generated　by　known　applications　or　protocols,　which　have　been　previously　identified　and　labeled,　and　unknown　network　traffic　that　cannot　be　identified　based　on　existing　knowledge.　Accurately　identifying　unknown　traffic　is　critical　to　network　management　and　security,　not　only　to　help　managers　allocate　bandwidth　appropriately　for　all　types　of　applications　and　ensure　quality　of　service,　but　also　to　prevent　security　breaches　that　may　result　from　unknown　applications　or　protocols.　Notably,　the　unknown　network　traffic　has　been　increasing　with　the　emergence　of　new　applications　or　protocols,　which　further　increases　the　difficulty　in　identifying　them.　Existing　unknown　traffic　classification　methods　based　on　Softmax　output　confidence　values　cause　bias　in　the　prediction　probability　due　to　overconfidence　of　the　model　during　the　training　process,　thus　decreasing　the　identification　accuracy.　Thus,　for　unknown　traffic　identification,　this　study　proposes　a　deep-learning-based　uncertainty-estimation　(EUE)　approach.　EUE　introduces　the　theory　of　evidence　to　the　task　of　identifying　unknown　traffic　by　inferring　traffic　uncertainty　directly　from　traffic　evidence　without　the　need　for　a　Softmax　layer,　thus　avoiding　overconfidence　in　the　model.　Thus,　the　EUE　can　accurately　identify　unknown　traffic　while　classifying　known　traffic　at　the　application　level.　We　construct　two　experimental　scenarios　simulating　the　real　network　environments　with　different　proportions　of　unknown　traffic　to　evaluate　EUE.　The　experimental　results　show　that　the　proposed　approach　EUE　exhibits　excellent　classification　accuracy.　IEEE