Educational Framework for Power Side-Channel Attacks on Neural Networks in Embedded Systems

We present an educational framework for security analysis of neural networks using the ChipWhisperer (CW) embedded system. More specifically, our contribution is to build a simple framework capable of performing power side-channel attacks from traces directly captured by CW’s microcontroller. CW eliminates the need for expensive and complex equipment like oscilloscopes, which helps to simplify the educational mission. Our work provides a modern educational tool, enabling students to learn about the real-world resilience of neural networks end-to-end, from training to deployment to security analysis, thereby contributing to the development of more secure systems in the future. In addition, we incorporate learning of software coding on embedded systems assisted by large language models.