Learning Device Models with Recurrent Neural Networks

In this paper we consider whether RNNs can learn functionally equivalent models of unknown computer hardware peripherals through input/output observation. Peripheral devices attach to a main computer and use both hardware within the device and driver software running on the main computer to perform a task, such as printing a page or sending a message. However, there are instances when hardware is accessible from the main system but driver software is not, rendering the peripheral unusable. This situation is prevalent in open source operating systems where driver software may not be available from the vendor. Without driver software or development documentation, it is incumbent on the system's owner to write software to make use of the peripheral. The device itself is a "black box", with no information directly available to the developer beyond a set of memory addresses to interact with the device and the observable output of the hardware itself. This leads to labor-intensive reverse engineering efforts with varying degrees of success (see e.g.