User identification and authentication using multi-modal behavioral biometrics

Biometric computer authentication has an advantage over password and access card authentication in that it is based on something you are, which is not easily copied or stolen. One way of performing biometric computer authentication is to use behavioral tendencies associated with how a user interacts with the computer. However, behavioral biometric authentication accuracy rates are worse than more traditional authentication methods. This article presents a behavioral biometric system that fuses user data from keyboard, mouse, and Graphical User Interface (GUI) interactions. Combining the modalities results in a more accurate authentication decision based on a broader view of the user’s computer activity while requiring less user interaction to train the system than previous work. Testing over 31 users shows that fusion techniques significantly improve behavioral biometric authentication accuracy over single modalities on their own. Between the two fusion techniques presented, feature fusion and an ensemble based classification method, the ensemble method performs the best with a False Acceptance Rate (FAR) of 2.10% and a False Rejection Rate (FRR) 2.24%.