However, many of the studies have focused on PC keyboard keystrokes.
More studies on mobile and smartphones keystroke dynamics are warranted; as smartphones make progress in both hardware and software, features from smartphones have been diversified.
Clearly, biometric data can be multifactored with PIN and pattern as a multifactor authentication.
However, the biggest problem with using biometric data is that the device should securely keep the biometric data in private: leakage of biometric data of a user invalidates lifetime use of the user’s biometric data as a private key.
We also demonstrate that opposite gender match between a legitimate user and impostors has influence on authenticating by our experiment results.
As we live in the smart era, the number of smartphone users grows every year [1, 2], whereas security measures to authenticate for an owner are standstill.
Our experiment result shows that keystroke dynamics are more effective in opposite gender imposters: FAR reduces when imposters are opposite gender compared to when imposters are the same gender.In Section 3, we discuss distance-based algorithms which are used to classify a legitimate user and how the classification works.In Section 4, we explain what features were extracted and the background of our experiment. Finally, we conclude our study and propose future work in Section 6.We develop application, collect user keystroke data, experiment classifications, and show our results. We collected user data samples and experimented keystroke dynamics using the most simple classification algorithm, distance-based algorithm.We experimented with both of Euclidean distance and Manhattan distance and obtained better performance with Manhattan distance, 7.89% EER (equal error rate).