Abstract: A new user authentication approach based on users’ keystroke patterns using manifold learning is proposed. The proposed approach utilizes Geodesic distance to denote the difference between sample vectors and then uses a new nonlinear dimensionality reduction algorithm: isometric mapping (ISOMAP) to find intrinsic geometry structure hiding in users’ keystroke patterns space. The performance of this approach is evaluated using 1500 keystroke sequences，the experimental results show the superiority of this approach in terms of false reject rate(FRR) and false accept rate(FAR) compared with some recent existing methods, the FRR and FAR of the proposed approach is only 1.65% and 0% respectively.