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Behavior Based Human Authentication on Touch Screen Devices Using Gestures and Signatures

With the rich functionalities and enhanced computing capabilities available on mobile computing devices with touch screens, users not only store sensitive information (such as credit card numbers) but also use privacy sensitive applications (such as online banking) on these devices, which make them hot targets for hackers and thieves. To protect private information, such devices typically lock themselves after a few minutes of inactivity and prompt a password/PIN/pattern screen when reactivated. Passwords/PINs/patterns based schemes are inherently vulnerable to shoulder surfing attacks and smudge attacks. In this paper, we propose BEAT, an authentication scheme for touch screen devices that authenticates users based on their behavior of performing certain actions on the touch screens. An action is either a gesture, which is a brief interaction of a user's fingers with the touch screen such as swipe rightwards, or a signature, which is the conventional unique handwritten depiction of one's name. Unlike existing authentication schemes for touch screen devices, which use what user inputs as the authentication secret, BEAT authenticates users mainly based on how they input, using distinguishing features such as velocity, device acceleration, and stroke time. Even if attackers see what action a user performs, they cannot reproduce the behavior of the user doing those actions through shoulder surfing or smudge attacks. We implemented BEAT on Samsung Focus smart phones and Samsung Slate tablets running Windows, collected 15,009 gesture samples and 10,054 signature samples, and conducted real-time experiments to evaluate its performance. Experimental results show that, with only 25 training samples, for gestures, BEAT achieves an average equal error rate of 0.5 percent with three gestures and for signatures, it achieves an average equal error rate of 0.52 percent with single signature.