# Learning Device Models with Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1805.07869 · HTML: https://mlsanity.com/paper/1805.07869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 1 of 1)

- [MLDS: A Dataset for Weight-Space Analysis of Neural Networks](https://mlsanity.com/paper/2104.10555/) (2021)

## Abstract (arXiv)

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.