Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

Abstract

Most of the current authentication mechanisms adopt the “one-time authentication,” which authenticate users for initial access. Once users have been authenticated, they can access network services without further verifications. In this case, after an illegal user completes authentication through identity forgery or a malicious user completes authentication by hijacking a legitimate user, his or her behaviour will become uncontrollable and may result in unknown risks to the network. These kinds of insider attacks have been increasingly threatening lots of organizations, and have boosted the emergence of zero trust architecture. In this paper, we propose a Multimodal Fusion-based Continuous Authentication (MFCA) scheme, which collects multidimensional behaviour characteristics during the online process, verifies their identities continuously, and locks out the users once abnormal behaviours are detected to protect data privacy and prevent the risk of potential attack. More specifically, MFCA integrates the behaviours of keystroke, mouse movement, and application usage and presents a multimodal fusion mechanism and trust model to effectively figure out user behaviours. To evaluate the performance of the MFCA, we designed and implemented the MFCA system and the experimental results show that the MFCA can detect illegal users in quick time with high accuracy.