In　order　to　improve　the　recognition　rate　of　users　with　single　behavioral　feature　and　prevent　impostors　from　restricting　an　input　device　to　avoid　detection,　a　dual-index　user　authentication　method　based　on　Multiple　Kernel　Learning　(MKL)　for　keystroke　and　mouse　behavioral　feature　fusion　was　proposed　in　this　paper.　Due　to　the　heterogeneity　between　the　keystroke　features　and　the　mouse　features,　we　argue　that　each　type　of　features　is　mapped　to　a　suitable　kernel　and　the　weights　of　each　kernel　are　obtained　through　computing　and　then　summed　to　obtain　a　compound　kernel　that　implements　the　multifeature　fusion.　The　dataset　used　in　this　paper　was　collected　under　complete　uncontrolled　condition　from　some　volunteers　by　using　our　data　collection　program.　The　experimental　results　show　that　the　proposed　method　can　obtain　the　best　recognition　accuracy　of　89.6%.　Compared　to　the　traditional　methods　of　single　feature,　the　dual-index　method　can　get　more　stable　and　effective　authentication.　Therefore,　the　proposed　method　in　this　paper　fully　demonstrates　the　reliability　of　dual-index　user　authentication.