Kollector: Detecting fraudulent activities on mobile devices using deep learning
With the rapid growth in smartphone usage, preventing leakage of personal information and privacy has become a challenging task. One major consequence of such leakage is impersonation. This type of illegal usage is nearly impossible to prevent as existing preventive mechanisms (e.g., passcode and fingerprinting), are not capable of continuously monitoring usage and determining whether the user is authorized. Once unauthorized users can defeat the initial protection mechanisms, they would have full access to the devices including using stored passwords to access high-value websites. We present Kollector, a new framework to detect impersonation based on a multi-view bagging deep learning approach to capture sequential tapping information on the smart-phone's keyboard. We construct a sequential-tapping biometrics model to continuously authenticate the user while typing. We empirically evaluated our system using real-world phone usage sessions from 26 users over eight weeks. We then compared our model against commonly used shallow machine techniques and find that our system performs better than other approaches and can achieve an 8.42 percent equal error rate, a 94.24 percent accuracy and a 94.41 percent H-mean using only the accelerometer and only five keyboard taps. We also experiment with using only three keyboard taps and find that the system still yields high accuracy while giving additional opportunities to make more decisions that can result in more accurate final decisions.
Sun, Lichao, Bokai Cao, Ji Wang, et al. 2021. "Kollector: Detecting Fraudulent Activities on Mobile Devices Using Deep Learning.” IEEE Transactions on Mobile Computing (TCM) 20(4): 1465-1476.
IEEE Transactions on Mobile Computing (TMC)