Multimodal Continuous User Authentication on Mobile Devices via Interaction Patterns

Abstract

Behavior-based continuous authentication is an increasingly popular methodology that utilizes behavior modeling and sensing for authentication and account access authorization. As an appearing behavioral biometric, user interaction patterns with mobile devices focus on verifying their identity in terms of their features or operating styles while interacting with devices. However, unimodal continuous authentication schemes, which are on the basis of a single source of interaction information, can only deal with a particular action or scenario. Hence, multimodal systems should be taken to suit for various environmental conditions especially in circumstances of attacks. In this paper, we propose a multimodal continuous authentication method both based on static interaction patterns and dynamic interaction patterns with mobile devices. Behavioral biometric features, HMHP, which is combined hand motion (HM) and hold posture (HP), are essentially established upon the touch screen and accelerator and capture the variation model of microhand motions and hold patterns generated in both dynamic and static scenes. By combining the features of HM and HP, the fusion feature HMHP achieves 97% accuracy with a 3.49% equal error rate.

Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning

“…Thus, it was used in the training and testing of the model. While all features performed with high accuracies, the fusion of hand motion and hand posture features was an average of 95% with a top accuracy of 99%, compared with the top accuracy of just hand motion which was around 84%, and the top accuracy of hand posture which was around 95% (Zhang et al, 2021). When comparing F1 scores it was confirmed that the fusion of the features achieved the best performance.…”

“…Authors in (Zhang et al, 2021), examined the use of phone movement dynamics as the sole biometric used in an authentication schema. Using the way a user motioned with their hand and the posture in which they held their device, the authors created a schema that used both dynamic and static iteration respectively to make an authentication decision.…”

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

“…Cao et al [16], Y. Wang et al [17], X. Zeng et al [18], and Q. Zou et al [19] utilize Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) algorithms for mobile gait recognition using smartphone sensors. These systems collect data using an accelerometer [16], or an accelerometer and a gyroscope [17][18][19].…”

Secure Multimodal Authentication Scheme for Wireless Sensor Networks

“…The scheme verified user identities constantly by collecting multidimensional behaviour characteristics through online procedure, and locks out users if any strange behaviours were noticed. Hand motion and hold posture were combined to capture static and dynamic interaction patterns with mobile devices for continuous authentication [42]. Fusion of features could achieve better accuracy and also reduce equal error rate.…”