Deep neural network modeling attacks on arbiter-PUF-based designs
Abstract Physical Unclonable Functions (PUFs) are novel circuit structures that provide hardware security solutions in application areas such as chip design and IoT, due to characteristics of their lightweight, key-free and tamper-resistant. PUFs are not immune to threats like machine learning model...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-02-01
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| Series: | Cybersecurity |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42400-024-00308-7 |
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| Summary: | Abstract Physical Unclonable Functions (PUFs) are novel circuit structures that provide hardware security solutions in application areas such as chip design and IoT, due to characteristics of their lightweight, key-free and tamper-resistant. PUFs are not immune to threats like machine learning modeling attacks and side channel modeling attacks. Strong PUFs are susceptible to classical machine learning attacks, however, machine learning’s effectiveness in attacking complex structured strong PUFs is limited, and its efficiency is relatively low. Side-channel modeling attacks, on the other hand, incur high implementation costs. Hence, employing deep learning for modeling attacks becomes an effective and cost-efficient choice when attacking complex structured PUFs. In this paper, we introduce a method that employs deep neural network to assess the modeling resilience of combination logic operation-based PUFs with APUFs as components for the first time. We employed a 4-layer DNN model to investigate the security resilience of PUF models involving any combination of OR AND and XOR logical operations. We explored the security regular patterns of modeling resilience. We have demonstrated for the first time that bias in PUF responses can reduce or destroy the security of PUFs. OR or AND logic operations do not provide any security benefit in PUF design, while XOR operations enhance the security of PUFs. |
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| ISSN: | 2523-3246 |