Robot Mimicry Attack on Keystroke-Dynamics User Identification and Authentication System

Future robots will be very advanced with high flexibility and accurate control performance. They will have the ability to mimic human behaviours or even perform better, which raises the significant risk of robot attack. In this work, we study the robot mimic attack on the current keystroke-dynamic user authentication system. Specifically, we proposed a robot mimicry attack framework for keystroke-dynamics systems. We collected keyboard logging data and acoustical signal data from real users and extracted the timing pattern of keystrokes to understand victim's behaviour for robot imitation attacks. Furthermore, we develop a deep Q-Network (DQN) algorithm to control the velocity of robot which is one of the key challenges of forging the human typing timing features. We tested and evaluated our approach on the real-life robotic testbed. We presented our results considering user identification and user authentication performance. We achieved a 90.3% user identification accuracy with genuine keyboard logging data samples and 89.6% accuracy with robot-forged data samples. Furthermore, we achieved 11.1%, and 36.6% EER for user authentication performance with zero-effort attack, and robot mimicry attack, respectively.