Keystroke　biometrics　is　a　well-investigated　dynamic　behavioral　methodology　that　utilizes　the　unique　behavioral　patterns　of　users　to　verify　their　identity　when　tapping　keys.　However,　the　performance　of　keystroke　biometrics　is　unreliable　due　to　its　high　error　rate　and　low　robustness.　In　this　paper,　we　propose　differential　evolution　and　adversarial　noise-based　user　authentication　(DEANUA),　which　is　a　verification　scheme　for　enhancing　reliability　by　reducing　the　error　rate　and　improving　robustness.　We　investigate　the　current　mainstream　features　and　build　a　more　comprehensive　feature　set　that　composed　of　146　features.　Then,　we　use　a　differential　evolution　method　to　select　an　optimized　feature　set.　With　the　support　vector　regression　method　on　this　feature　set,　we　achieve　an　equal　error　rate　(EER)　of　0.12660%　and　also　a　31.25%　energy　consumption　reduction　rate.　In　this　paper,　the　model　is　trained　with　the　training　samples　collected　from　one　situation,　but　the　model　is　used　in　various　situations.　Thus,　the　robustness　of　the　model　is　inadequate.　We　constructed　the　adversarial　noise　samples　to　simulate　users＇　behavioral　characteristics　in　different　situational　contexts.　We　use　the　adversarial　noise　samples　to　test　the　models　in　a　strict　experimental　environment,　which　raises　the　EER　by　83.59%,　to　10.9299%.　Then,　we　enhance　the　model　with　adversarial　noise　samples　to　obtain　an　EER　of　8.70932%,　which　is　a　reduction　of　20.32%.