User identification and authentication using multi-modal behavioral biometrics

• Novel fusion of keyboard, mouse, and GUI behavioral modalities for online authentication.
• First comparison testing of all three modalities with nearly free computer use.
• Reduced input requirements to 987 keystrokes, 28 mouse, and 85 GUI events.
• Ensemble classifier yields significantly better FAR and FRR over individual modalities.

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%.

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