The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks
The design of a silicon Strong Physical Unclonable Function (PUF) that is lightweight and stable, and which possesses a rigorous security argument, has been a fundamental problem in PUF research since its very beginnings in 2002. Various effective PUF modeling attacks, for example at CCS 2010 and CH...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ruhr-Universität Bochum
2019-08-01
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| Series: | Transactions on Cryptographic Hardware and Embedded Systems |
| Subjects: | |
| Online Access: | https://tches.iacr.org/index.php/TCHES/article/view/8351 |
| _version_ | 1811311978149314560 |
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| author | Phuong Ha Nguyen Durga Prasad Sahoo Chenglu Jin Kaleel Mahmood Ulrich Rührmair Marten van Dijk |
| author_facet | Phuong Ha Nguyen Durga Prasad Sahoo Chenglu Jin Kaleel Mahmood Ulrich Rührmair Marten van Dijk |
| author_sort | Phuong Ha Nguyen |
| collection | DOAJ |
| description | The design of a silicon Strong Physical Unclonable Function (PUF) that is lightweight and stable, and which possesses a rigorous security argument, has been a fundamental problem in PUF research since its very beginnings in 2002. Various effective PUF modeling attacks, for example at CCS 2010 and CHES 2015, have shown that currently, no existing silicon PUF design can meet these requirements. In this paper, we introduce the novel Interpose PUF (iPUF) design, and rigorously prove its security against all known machine learning (ML) attacks, including any currently known reliability-based strategies that exploit the stability of single CRPs (we are the first to provide a detailed analysis of when the reliability based CMA-ES attack is successful and when it is not applicable). Furthermore, we provide simulations and confirm these in experiments with FPGA implementations of the iPUF, demonstrating its practicality. Our new iPUF architecture so solves the currently open problem of constructing practical, silicon Strong PUFs that are secure against state-of-the-art ML attacks. |
| first_indexed | 2024-04-13T10:28:15Z |
| format | Article |
| id | doaj.art-59ebc9cb625c43d69be8645cbb5e73cd |
| institution | Directory Open Access Journal |
| issn | 2569-2925 |
| language | English |
| last_indexed | 2024-04-13T10:28:15Z |
| publishDate | 2019-08-01 |
| publisher | Ruhr-Universität Bochum |
| record_format | Article |
| series | Transactions on Cryptographic Hardware and Embedded Systems |
| spelling | doaj.art-59ebc9cb625c43d69be8645cbb5e73cd2022-12-22T02:50:14ZengRuhr-Universität BochumTransactions on Cryptographic Hardware and Embedded Systems2569-29252019-08-012019410.13154/tches.v2019.i4.243-290The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning AttacksPhuong Ha Nguyen0Durga Prasad Sahoo1Chenglu Jin2Kaleel Mahmood3Ulrich Rührmair4Marten van Dijk5University of ConnecticutBosch India (RBEI/ESY)University of ConnecticutUniversity of ConnecticutLMU MünchenUniversity of ConnecticutThe design of a silicon Strong Physical Unclonable Function (PUF) that is lightweight and stable, and which possesses a rigorous security argument, has been a fundamental problem in PUF research since its very beginnings in 2002. Various effective PUF modeling attacks, for example at CCS 2010 and CHES 2015, have shown that currently, no existing silicon PUF design can meet these requirements. In this paper, we introduce the novel Interpose PUF (iPUF) design, and rigorously prove its security against all known machine learning (ML) attacks, including any currently known reliability-based strategies that exploit the stability of single CRPs (we are the first to provide a detailed analysis of when the reliability based CMA-ES attack is successful and when it is not applicable). Furthermore, we provide simulations and confirm these in experiments with FPGA implementations of the iPUF, demonstrating its practicality. Our new iPUF architecture so solves the currently open problem of constructing practical, silicon Strong PUFs that are secure against state-of-the-art ML attacks.https://tches.iacr.org/index.php/TCHES/article/view/8351Arbiter Physical Unclonable Function (APUF)Majority VotingModeling AttackStrict Avalanche CriterionReliability based ModelingXOR APUF |
| spellingShingle | Phuong Ha Nguyen Durga Prasad Sahoo Chenglu Jin Kaleel Mahmood Ulrich Rührmair Marten van Dijk The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks Transactions on Cryptographic Hardware and Embedded Systems Arbiter Physical Unclonable Function (APUF) Majority Voting Modeling Attack Strict Avalanche Criterion Reliability based Modeling XOR APUF |
| title | The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks |
| title_full | The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks |
| title_fullStr | The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks |
| title_full_unstemmed | The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks |
| title_short | The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks |
| title_sort | interpose puf secure puf design against state of the art machine learning attacks |
| topic | Arbiter Physical Unclonable Function (APUF) Majority Voting Modeling Attack Strict Avalanche Criterion Reliability based Modeling XOR APUF |
| url | https://tches.iacr.org/index.php/TCHES/article/view/8351 |
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