Safeguard: User Reauthentication on Smartphones via Behavioral Biometrics

With the emergence of smartphones as an essential part of daily life, the demand for user reauthentication has increased manifolds. The effective and widely practiced biometric schemes are based upon the principle of “who you are” which utilizes inherent and unique characteristics of the user. In this context, the behavioral biometrics such as sliding dynamics and pressure intensity make use of on-screen sliding movements to infer the user´s patterns. In this paper, we present Safeguard, an accurate and efficient smartphone user reauthentication (verification) system based upon on-screen finger movements. The computation and processing is performed at back-end which is transparent to the users. The key feature of the proposed system lies in fine-grained on-screen biometric metrics, i.e., sliding dynamics and pressure intensity, which are unique to each user under diverse scenarios. We first implement our scheme through five machine learning approaches and finally select the support vector machine (SVM)-based approach due to its high accuracy. We further analyze Safeguard to be robust against adversary imitation. We validate the efficacy of our approach through implementation on off-the-shelf smartphone followed by practical evaluation under different scenarios. We process a set of more than 50 000 effective samples derived from a raw dataset of over 10 000 slides collected from each of the 60 volunteers over a period of one month. The experimental results show that Safeguard can verify a user with 0.03% false acceptance rate (FAR) and 0.05% false rejection rate (FRR) within 0.3 s with 15 to 20 slides by the user. The FRR of our system adequately meets the European Standard for Access Control Systems, whereas FAR differs by 0.029%. Our future works aim to integrate multitouch sliding movements in existing scheme.