Malicious　encrypted　traffic　poses　great　threat　to　cyber　security　owing　to　encryption　and　the　ability　to　bypass　traditional　traffic　detection　schemes.　Malicious　encrypted　traffic　identification　is　a　challenging　task　and　has　attracted　researchers＇　attention　nowadays.　Existing　research　way　mainly　extracts　various　statistical　features　of　data-flow,　which　relies　artificial　experience　heavily.　To　round　the　above　problem.　a　fingerprint　enhancement　and　second-order　Markov　chain　based　scheme　is　proposed　in　this　paper,　obtaining　features　more　easily.　Fingerprint　enhancement　is　done　to　replace　SSL　fingerprint　by　refining　data-flow＇s　behavior.　Then　enhanced　fingerprint　is　fed　to　second-order　Markov　chain　to　obtain　dominating　feature　for　identification　model.　To　our　best　knowledge,　this　paper　is　the　first　one　focusing　on　using　fingerprint　and　second　order　Markov　chain　to　simplify　feature　extraction.　Finally,　the　proposed　scheme　is　verified　based　on　public　dataset　Stratosphere　IPS.　©　2020　ACM.