  With the recent advancements in machine learning theory, many commercial
embedded micro-processors use neural network models for a variety of signal
processing applications. However, their associated side-channel security
vulnerabilities pose a major concern. There have been several proof-of-concept
attacks demonstrating the extraction of their model parameters and input data.
But, many of these attacks involve specific assumptions, have limited
applicability, or pose huge overheads to the attacker. In this work, we study
the side-channel vulnerabilities of embedded neural network implementations by
recovering their parameters using timing-based information leakage and simple
power analysis side-channel attacks. We demonstrate our attacks on popular
micro-controller platforms over networks of different precisions such as
floating point, fixed point, binary networks. We are able to successfully
recover not only the model parameters but also the inputs for the above
networks. Countermeasures against timing-based attacks are implemented and
their overheads are analyzed.
