On the user keyboard usage behavior analysis

Although many services available today in emerging information technologies started to use fingerprint, electronic key, face recognition methods to identify authorized or privileged users, still the majority usage of such services requires keyboard typed password. In order to increase the security and prevent unwanted accesses, security authorities mandate users to pick a password that has enough complexity and change it periodically but by ignoring the share of the password intentionally or unintentionally with third persons that workarounds all complexity relied on for security. This thesis is the part of more general topic that is Privileged Access Management (PAM). Firstly, this study examines user keyboard behavior, extracting features from the data collected during brief text entry, and attempts to authenticate the user's identity using various classification methods. The performance metrics of these methods are then calculated and compared. Secondly, in addition to classifiers found in the literature, a new GRU-based Siamese network architecture has been designed. The results of this architecture have been analyzed in the presence of leakage, and a method that completely prevents leakage has been proposed and the results shared. Finally, since the PAM project aims to offer all its services and functions in a cloud environment, CMU's keystroke database will be simulated in a network environment through a real-time embedded system. The performance of the proposed classification methods will be examined and compared under the conditions of new data collected using protocols such as UDP, TCP, and SSL.