Abstract: An Internet-of-Things (IoT) learning framework (IoT learning framework) may train AI models to infer user activities from IoT connected device events using machine learning based algorithms. According to such an example, processing circuitry obtains a training dataset indicating IoT device events and extracts representative user activity patterns from the sequences of IoT device events. In such an example, processing circuitry trains the AI model to learn an optimal subset of the sequences of IoT device events corresponding to a smallest quantity of the sequences of IoT device events to predict user activities with accuracy that satisfies a threshold and outputs the AI model. According to such an example, processing circuitry may obtain new data indicating new sequences of IoT device events and generates output indicating one or more user activities predicted by the AI model to have occurred based on the new sequences of IoT device events.

Abstract: A system includes a processor that accesses a sequence of device events S from IoT network traffic and a plurality of user activity signatures, each user activity signature U in the plurality of user activity signatures being a known sequence of device events. The processor is configured to infer a user activity by way of approximate user activity signature matching.

Abstract: Systems and methods for authenticating a user through an unobservable re-authentication process are disclosed.

Abstract: Systems and methods for authenticating a user through an unobservable re-authentication process are disclosed.