Network　traffic　classification,　the　task　of　associating　network　traffic　with　their　generating　application　protocols　or　applications,　is　valuable　for　the　control,　allocation,　and　management　of　resources　in　today＇s　TCP/IP　networks.　In　this　paper,　we　propose　Ulfar,　a　multi-scale　feature　attention　approach　to　network　traffic　classification,　which　uses　convolutional　neural　networks　(CNN)　as　the　building　block　of　the　deep　packet　analysis　model.　In　Ulfar,　we　take　only　one　packet　per　flow　for　network　traffic　classification.　Ulfar　is　based　on　the　key　insight　that　format-related　bytes　appear　at　fixed　offsets　or　in　a　specific　pattern　in　the　IP　packet,　and　these　format-related　bytes　are　important　for　accurate　network　traffic　classification.　Our　neural　network　model　can　automatically　recover　the　format-related　bytes　by　building　high-level,　multi-scale　n-gram　features　from　raw　byte　sequences.　In　addition,　at　the　representation　learning　side,　we　try　to　understand　what　patterns　and　signatures　our　neural　network　model　learns　from　network　traffic.　We　evaluate　Ulfar　using　two　publicly　available　datasets,　and　our　experimental　results　show　that　Ulfar　can　conduct　accurate　network　traffic　classification.　Also,　we　compare　the　results　of　Ulfar　with　four　state-of-the-art　approaches,　and　find　that　Ulfar　has　the　ability　to　classify　network　traffic　more　accurately.