In　recent　years,　the　gradual　popularization　of　mobile　terminals　and　the　vigorous　development　of　the　network　have　spawned　the　birth　of　a　new　Internet　structure　and　promoted　the　growth　of　network　traffic.　Behind　such　a　large　network,　effective　supervision　of　network　traffic　is　the　cornerstone　of　network　security　protection.　At　present,　many　studies　on　the　direction　of　network　supervision　focus　on　the　analysis　of　unknown　network　protocol　types.　The　protocol　identification　method　combined　with　machine　learning　is　a　hot　topic　in　this　kind　of　research.　This　method　extracts　data　stream　features　and　builds　data　sets,　using　machine　learning　algorithms.　The　model　analyzes　unknown　network　traffic　and　can　obtain　better　recognition　results　than　traditional　network　protocol　analysis　methods.　Aiming　at　the　problem　of　unknown　traffic　identification,　this　paper　proposes　a　reasonable　unknown　traffic　identification　algorithm.　The　feature　normalization　preprocessing,　feature　selection,　LOF　outlier　analysis,　etc.　are　introduced.　The　clustering　process　uses　the　K-Means++　algorithm,　and　the　maximum　local　reachable　density　point　in　the　outlier　analysis　is　used　to　realize　the　initial　cluster　center　point.　Accurate　positioning.　©　2020　Journal　of　Physics:　Conference　Series.