Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156095
Title: Physical security of deep learning on edge devices : comprehensive evaluation of fault injection attack vectors
Authors: Hou, Xiaolu
Breier, Jakub
Jap, Dirmanto
Ma, Lei
Bhasin, Shivam
Liu, Yang
Keywords: Engineering::Computer science and engineering::Hardware::Performance and reliability
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Source: Hou, X., Breier, J., Jap, D., Ma, L., Bhasin, S. & Liu, Y. (2021). Physical security of deep learning on edge devices : comprehensive evaluation of fault injection attack vectors. Microelectronics Reliability, 120, 114116-. https://dx.doi.org/10.1016/j.microrel.2021.114116
Project: NRF2018NCR-NCR009- 0001 
Journal: Microelectronics Reliability 
Abstract: Decision making tasks carried out by the usage of deep neural networks are successfully taking over in many areas, including those that are security critical, such as healthcare, transportation, smart grids, where intentional and unintentional failures can be disastrous. Edge computing systems are becoming ubiquitous nowadays, often serving deep learning tasks that do not need to be sent over to servers. Therefore, there is a necessity to evaluate the potential attacks that can target deep learning in the edge. In this work, we present evaluation of deep neural networks (DNNs) reliability against fault injection attacks. We first experimentally evaluate DNNs implemented in an embedded device by using laser fault injection to get the insight on possible attack vectors. We show practical results on four activation functions, ReLu, softmax, sigmoid, and tanh. We then perform a deep study on DNNs based on derived fault models by using several different attack strategies based on random faults. We also investigate a powerful attacker who can find effective fault location based on genetic algorithm, to show the most efficient attacks in terms of misclassification success rates. Finally, we show how a state of the art countermeasure against model extraction attack can be bypassed with a fault attack. Our results can serve as a basis to outline the susceptibility of DNNs to physical attacks which can be considered a viable attack vector whenever a device is deployed in hostile environment.
URI: https://hdl.handle.net/10356/156095
ISSN: 0026-2714
DOI: 10.1016/j.microrel.2021.114116
Rights: © 2021 Elsevier Ltd. All rights reserved. This paper was published in Microelectronics Reliability and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20230607
Fulltext Availability: With Fulltext
Appears in Collections:TL Journal Articles

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