Biometric　authentication　has　advantages　over　traditional　authentication　based　on　passwords　or　pin　number　(PIN)　in　that　it　is　based　on　the　user＇s　inherent　characteristics　which　is　not　easily　stolen　or　lost.　Keystroke　dynamics　and　mouse　dynamics　are　biometrics　that　study　the　behavior　patterns　of　human-computer　interaction　(HCI).　Personal　keystroke　pattern　and　mouse-movement　pattern　are　difficult　to　imitate　and　can,　therefore,　be　used　for　personal　identity　authentication.　Keystrokes　and　mouse　movements　can　potentially　authenticate　users　without　affecting　the　use　of　computers　and　other　devices　to　improve　system　security.　In　real　environments,　authentication　methods　that　fuse　keystroke　dynamics　and　mouse　dynamics　are　less　accurate.　In　this　paper,　a　new　method　of　user　authentication　using　complex　real-environment　HCI　data　is　presented,　which　is　called　authentication　adaptation　network　(AAN).　In　this　method,　heterogeneous　domain　adaptation　(HDA)　method　is　used　for　user　authentication　based　on　keystroke　dynamics　and　mouse　dynamics　for　the　first　time.　All　representative　time　windows　and　dimensionality　reduction　targets　of　keystroke　dynamics　features　are　compared　to　determine　the　parameters　of　AAN　to　ensure　the　robustness　of　the　algorithm,　and　the　effectiveness　of　the　algorithm　is　demonstrated　by　validation　experiments　and　comparison　with　the　methods　proposed　in　previous　studies.　Finally,　experiments　using　the　collected　real-environment　HCI　dataset　obtained　89.22%　user　authentication　accuracy,　which　indicate　that　the　proposed　method　achieves　an　encouraging　performance.