Due to the rapid advancement of technology and the growing usage need for computer networks the number of users are increasing and their actions are becoming more sophisticated. Several studies show a strong correlation between a user's real interests and how they utilize social networking sites and the internet. By examining the user's typing activity, one can gain an understanding of their interests and habits. The click stroke analysis approach, which enables us to identify new customers and raise the standard of information services provided to clients, is the main contribution of this study. The user behavior analysis is studied and during this stage, the differences in user typing behavior in various access events is analyzed and a machine learning technique is applied to identify the user's anomalous behavior. In the cognitive model's data processing layer, classification and prediction algorithms are used. logger are taken as datasets for classifying the behavior of the user.