Learning Device Models with Recurrent Neural Networks
This enables automated device modeling for tasks like validation and reverse engineering, but it is incremental as it applies existing RNN methods to a new domain.
The paper tackled the problem of learning functional software-only models of hardware devices from input and output state observations using recurrent neural networks (RNNs), and the result showed that RNNs successfully modeled six different devices, including a 16550 UART serial port, with verified equivalent output to real hardware.
Recurrent neural networks (RNNs) are powerful constructs capable of modeling complex systems, up to and including Turing Machines. However, learning such complex models from finite training sets can be difficult. In this paper we empirically show that RNNs can learn models of computer peripheral devices through input and output state observation. This enables automated development of functional software-only models of hardware devices. Such models are applicable to any number of tasks, including device validation, driver development, code de-obfuscation, and reverse engineering. We show that the same RNN structure successfully models six different devices from simple test circuits up to a 16550 UART serial port, and verify that these models are capable of producing equivalent output to real hardware.