Publication details
CNN architecture extraction on edge GPU
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | Artificial Intelligence in Hardware Security (AIHWS) Workshop (Satellite Workshops held in parallel with the 22nd International Conference on Applied Cryptography and Network Security, ACNS 2024) |
| MU Faculty or unit | |
| Citation | |
| Doi | https://doi.org/10.1007/978-3-031-61486-6_10 |
| Keywords | Deep Learning; NVIDIA GPU; Side Channel Attack |
| Description | Neural networks have become popular due to their versatility and state-of-the-art results in many applications, such as image classification, natural language processing, speech recognition, forecasting, etc. These applications are also used in resource-constrained environments such as embedded devices. In this work, the susceptibility of neural network implementations to reverse engineering is explored on the NVIDIA Jetson Nano microcomputer via side-channel analysis. To this end, an architecture extraction attack is presented. In the attack, 15 popular convolutional neural network architectures (EfficientNets, MobileNets, NasNet, etc.) are implemented on the GPU of Jetson Nano and the electromagnetic radiation of the GPU is analyzed during the inference operation of the neural networks. The results of the analysis show that neural network architectures are easily distinguishable using deep learning-based side-channel analysis. |