BANN-TMGuard: Toward Touch-Movement-Based Screen Unlock Patterns via Blockchain-Enabled Artificial Neural Networks on IoT Devices

Internet of Things (IoT) devices, such as smartphones, have become important to people’s everyday usage, especially the number of smartphone shipment has surpassed six billion and is forecast to further grow. The smartphone security is the top priority as people may store various sensitive information on these devices. Currently, phone unlock patterns, e.g., Android unlock patterns, are one of the main protection methods to protect smartphones from unauthorized access. However, many research studies have revealed that cyber-attackers can easily compromise this type of unlock mechanism, i.e., learning the pattern from the touch residue. In this work, we advocate that an additional security layer should be added to enhance the security of Android unlock patterns, and thus develop a touch movement-based unlock mechanism via blockchain-enabled artificial neural networks (ANNs), named BANN-TMGuard, which can examine the biometric features of a user’s touch movement as well as the input pattern. Further, BANN-TMGuard adopts blockchain technology to secure the robustness and reliability when building the ANN models. In the evaluation, we perform a user study with 100 participants in the aspects of authentication accuracy, time consumption and user feedback. As compared with similar schemes, our BANN-TMGuard demonstrates better results and is preferred by most participants in the user study.