Reverse Engineering Behavioural Models of IoT Devices

This paper addresses the problem of recovering behavioural models from IoT devices in order to help engineers understand how they are functioning and audit them. We present a model learning approach called ASSESS, which takes as inputs execution traces collected from IoT devices and generates models called systems of Labelled Transition Systems (LTSs). ASSESS generates as many LTSs as components integrated and identified into a device. The approach is specialised to IoT devices as it takes into account two architectures often used to integrate components with this kind of system (cyclic functioning, loosely-coupled or decoupled architectures). We experimented the approach on two IoT devices and an IoT gateway to evaluate the model conciseness and the approach efficiency.