ADEAS: Authentication Using Doppler Effect of Acoustic Signals Caused by Hands Moving

Presently, the prevalent authentication approaches in smartphones are susceptible to interference from light,0 noise, temperature, and the risk of replay attacks. In light of these vulnerabilities, and taking into account user behavior alongside smartphone interaction patterns, we have developed an innovative behavioral-based authentication system. This system harnesses the distinctiveness of individual keystroke dynamics for secure user authentication, offering resilience against noise and light fluctuations. In this unique approach, our smartphone’s speakers and microphones emit and capture high-frequency acoustic signals (AS). To the best of our knowledge, this is the first instance of employing the Doppler effect generated by the high-frequency AS in response to keystroke activity as a distinctive user feature. Our definition of ’keystroke behavior’ encompasses the motions involved in tapping screen buttons while holding the smartphone, effectively capturing unique user attributes without necessitating any special procedures or passwords. Our initial experiments have convincingly shown that the AS Doppler effect, triggered by keystroke actions, is uniquely identifiable per user during button presses. Subsequently, we utilized a Convolutional Autoencoder (CAE) to distill keystroke behaviors from the reflected signals, employing a One-Class Support Vector Machine (OCSVM) for user authentication and identification processes. We then implemented a prototype of this scheme on smartphones and rigorously tested its performance across four real-world scenarios. The outcomes are promising, demonstrating that our scheme not only withstands disturbances from noise and light but also achieves an impressive average accuracy rate of 95.08%. Regarding security, it effectively thwarts replay and record attacks, further underscoring its robustness and reliability.