A neural-statistical modeling approach for keystroke recognition algorithms

The main problem of the computer and information systems is the security, which is to protectthe system from the attacks of imposter or unauthorized users. In order to supply bettersecurity, it must be determined clearly while system access that if the claimed one isauthorized user known by the system or not.Recently, biometric security systems technology is developed and added to the typicalauthentication systems , which are consist of username and PIN or password query, aiming toget higher security in system access.The keystroke pattern recognition system is chosen as one of the biometric security systemand proposed to perform a classification in this thesis. In order to achieve this, a perspective isdeveloped under the knowledge of the classification algorithms used earlier in keystrokepattern recognition systems. According to this, a model is designed which uses hybridcombination of two different algorithms. One of them is the statistical algorithm which is thevery firstly used one in pattern recognition and the other one is the neural networks. In themodel, the statistical algorithm formulations are embedded into the neural networkarchitecture. Designed algorithm model is described in detail and tested with sample userdatasets and performance results are presented.When thinking about need of new approaches in the classification algorithms in keystrokepattern recognition, this study can be a starting point to further enhancements with itsperspective on the subject.