User Authentication Based On Keystroke Sequence Features Extracted

Authentication based on keystroke pattern is an important method to safeguard computer information. In 1980's, Gaines, etc. presented an authentication approach based on keystroke dynamics in the first time. This approach is easy to be realized and cheap to be applied. But a significant problem is how to efficiently extract key information from the keystroke sequence and then decrease error rate. For this purpose, researchers have been doing a lot of work and many approaches have been proposed: approaches based on neural networks, support vector machine, genetic algorithm, etc. However, these approaches more or less share the defects of long convergence time and large training set. It is for that reason, these approaches cannot be applied in real time detection. To conquer these defects, a new approach based on continuous statistical model has been presented. With the merit of small training set and fast convergence rate, it can successfully hurdle those shortcomings. But this kind of approach also processes a disadvantage: its error rate is relatively high and preprocessing is needed in order to get low error rate. Aim at this demerit, based on Bayesian statistical algorithm, this paper abstracts pattern recognition of keystroke dynamics to three-class recognition, that is, intrusion class, suspicious class and normal class. Then mechanism of reorganization is applied to the suspicious class. Moreover, the range and criteria of safe rate for the system is provided through statistical deduction. The difference between classic approach and the new approach is theoretically discussed. The experimental result shows that the false reject rate and false accept rate are both decreased.However, a question is should the Euclidean distance be a necessary measurement for difference between pattern set? In the latter research, geodesic distance has been found to be a more ideal measurement. Meanwhile, illuminated by the research that there exists connection between memory(neural manifold) and visual manifold, a similar connection between memory and haptic manifold is proposed. Based on these, manifold learning algorithm is introduced. The new approach applies geodesic distance and provides theoretical range of the length of key. Experimental result proves that the FRR and FAR are further decreased.