With the rising cases of security issues, it has become a major concern and a matter worthy of note that having a reliable way of ensuring safe activities online or offline is an essential commodity in our present day. It is therefore important to consider safety a priority as individuals use their smartphones and mobile devices since these devices have come to be part of our lives. This research therefore focuses on how what an individual has, that is, how a behavioral pattern which is unique to every individual can be harnessed to ensure security and privacy of data and information. In doing this, Decision Tree (DT) and K-Nearest Neighbor (KNN), both machine learning algorithms were implemented and used to analyze the features of different people’s typing patters. A static password was used and every subject was required to type the password into a smartphone in order to capture their typing features. Afterwards, the required features were extracted and further analyzed for the purpose of use for security. At the end of our experiments, the results came out with an accuracy of 99.12% and 99.92% from KNN and DT respectively.

Decision Trees K-Nearest Neighbor Recall Accuracy False Positive