User Authentication leveraging behavioral information using Commodity WiFi devices

User authentication is a major area of interest within the field of Human Computer Interaction (HCI). Meanwhile, it prevents unauthorized accesses to certain the security of data. Personal Identification Number (PIN) and biometrics are the main approaches for identifying the user on the basis of his/her identity. However, PIN can be easily leaked to others, and biometrics usually require specialized devices. In this paper, we prototype our system, a new method for user authentication by leveraging commodity WiFi. The basic methodology is to explore the typing habit of users from Channel State Information (CSI). The design and implementation of our system face two challenges, i.e. extracting keystroke features from wireless channel data and authenticating the user via typing habit from the corresponding keystroke features. For the former, we capture signal fluctuations caused by the micro movements like typing and extract the keystroke features on channel response obtained from commodity WiFi devices. For the latter, we design a computational intelligence driven mechanism to authenticate users from the corresponding keystroke feature. We prototype our system on the low-cost off-the-shelf WiFi devices and evaluate its performance in real-world experiments. We have explored four classifiers including K Nearest Neighbor(KNN), Support Vector Machine (SVM), Random Forest, and Decision Tree for recognizing users. Empirical results show that KNN provides the best performance, i.e., 85.2% authentication accuracy, 12.8% false accept rate, and 11.2% false reject rate on average over 9 participants.