CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel

Authors: 

Lejla Batina, Radboud University, The Netherlands; Shivam Bhasin and Dirmanto Jap, Nanyang Technological University, Singapore; Stjepan Picek, Delft University of Technology, The Netherlands

Abstract: 

Machine learning has become mainstream across industries. Numerous examples prove the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using side-channel information such as timing and electromagnetic (EM) emanations. To this end, we consider multilayer perceptron and convolutional neural networks as the machine learning architectures of choice and assume a non-invasive and passive attacker capable of measuring those kinds of leakages.

We conduct all experiments on real data and commonly used neural network architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM Cortex-M3 microcontroller, which is a platform often used in pervasive applications using neural networks such as wearables, surveillance cameras, etc. Our experiments show that a side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using merely side-channel information such as timing or EM.

USENIX Security '19 Open Access Videos Sponsored by
King Abdullah University of Science and Technology (KAUST)

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@inproceedings {236204,
author = {Lejla Batina and Shivam Bhasin and Dirmanto Jap and Stjepan Picek},
title = {{CSI} {NN}: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel},
booktitle = {28th {USENIX} Security Symposium ({USENIX} Security 19)},
year = {2019},
isbn = {978-1-939133-06-9},
address = {Santa Clara, CA},
pages = {515--532},
url = {https://www.usenix.org/conference/usenixsecurity19/presentation/batina},
publisher = {{USENIX} Association},
month = aug,
}

Presentation Video