TouchSense: Accurate and Transparent User Re-authentication via Finger Touching

Abstract

Re-authentication identifies the user during the whole usage to enhance the security of smartphones. To avoid frequent interrupts to users, user features should be imperceptibly collected for identification without user assistance. Conventionally, behavior habits (e.g. movement, trail) during the user operation are commonly considered as the most appropriate features for re-authentication. The behavior features, however, are often fluctuating and inevitably sacrifice the accuracy of re-authentication, which puts the phones at risk increasingly. In this paper, we propose TouchSense, an accurate and transparent scheme for user re-authentication. The basic idea is to leverage the combined information of human biometric capacitance and touching behavior for user identification. When the user touches capacitive-based sensors, both information can be automatically collected and applied in the authentication, which is transparent to the user. Based on the authentication results, we build up user-legitimate models to comprehensively evaluate the user’s legitimacy, which reduces misjudgment and further improves accuracy. Moreover, we implement TouchSense on an SX9310 EVKA board and conduct comprehensive experiments to evaluate it. The results illustrate that TouchSense can identify 98% intruders within 10 s, but for legitimate users, the misjudgment is less than 0.9% in 2.6-hours-usage.