Active authentication with reinforcement learning based on ambient radio signals

Abstract

Active authentication of mobile devices such as smartphones and ipads is promising to enhance security to access confidential data or systems. In this paper, we propose an active authentication scheme, which exploits the physical-layer properties of ambient radio signals to identify mobile devices in indoor environments. More specifically, we discriminate mobile devices in different locations by analyzing the ambient radio sources, because the received signal strength indicator set of the ambient signals measured by a smartphone is usually different from that observed by its spoofer located in another area. We formulate the interactions between the legitimate mobile device and its spoofer as an active authentication game, in which the receiver chooses its test threshold in the hypothesis test in the spoofing detection, while the spoofer chooses its attack strength. In a dynamic radio environment with unknown attack parameters, we propose a learning-based authentication algorithm based on the physical-layer properties of the ambient radio environments. Simulation results show that the proposed scheme accurately detects spoofers in typical indoor environments.