Anomalies　in　packet　length　sequences　caused　by　network　topology　structure　and　congestion　greatly　impact　the　performance　of　early　network　traffic　classification.　Additionally,　insufficient　differentiation　of　packet　length　sequences　using　a　small　number　of　packets　also　affects　the　performance.　In　this　letter,　we　propose　SePeric,　a　packet　sequence　permutation-aware　approach　to　robust　network　traffic　classification.　By　exploring　the　correlations　within　packet　length　sequences　and　adjusting　them　to　eliminate　the　effects　of　anomalous　sequence　orders,　as　well　as　extracting　additional　features　from　the　byte　sequence　of　the　first　packet　to　supplement　the　insufficient　differentiation　in　packet　length　sequences.　©　2019　IEEE.