A Generic Obfuscation Framework for Preventing ML-Attacks on Strong-PUFs through Exploitation of DRAM-PUFs-Reference-Cited by-同舟云学术

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A Generic Obfuscation Framework for Preventing ML-Attacks on Strong-PUFs through Exploitation of DRAM-PUFs

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)
language:
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Author:

Millwood Owen¹,Pehlivanoğlu Meltem Kurt²,Mohammadi Pasikhani Aryan¹,Miskelly Jack³,Gope Prosanta¹,Kavun Elif Bilge⁴

Affiliation:

1. The University of Sheffield,Department of Computer Science,Sheffield,UK

2. Kocaeli University,Department of Computer Engineering,Kocaeli,Turkey

3. Queen’s University Belfast,Centre for Secure Information Technologies,Belfast,United Kingdom

4. University of Passau,Secure Intelligent Systems Research Group,Passau,Germany

Funder

Engineering and Physical Sciences Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10190357/10190476/10190495.pdf?arnumber=10190495

Reference43 articles.

1. Controlled physical random functions and applications

2. PUF Modeling Attacks on Simulated and Silicon Data

3. Silicon physical random functions

4. Modeling attacks on physical unclonable functions

5. A Scalable Protocol Level Approach to Prevent Machine Learning Attacks on Physically Unclonable Function Based Authentication Mechanisms for Internet of Medical Things

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