Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks
Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/5/537 |
| _version_ | 1797395359050235904 |
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| author | Hongxiang Gu Miodrag Potkonjak |
| author_facet | Hongxiang Gu Miodrag Potkonjak |
| author_sort | Hongxiang Gu |
| collection | DOAJ |
| description | Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN. |
| first_indexed | 2024-03-09T00:33:19Z |
| format | Article |
| id | doaj.art-43d2eb614cbc4f58a5473949628587e9 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T00:33:19Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-43d2eb614cbc4f58a5473949628587e92023-12-11T18:20:31ZengMDPI AGElectronics2079-92922021-02-0110553710.3390/electronics10050537Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF NetworksHongxiang Gu0Miodrag Potkonjak1Computer Science Department, University of California, Los Angeles, CA 90095, USAComputer Science Department, University of California, Los Angeles, CA 90095, USAPhysical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.https://www.mdpi.com/2079-9292/10/5/537physically unclonable functionshardware securityinternet of thingsreconfigurableevolution strategies |
| spellingShingle | Hongxiang Gu Miodrag Potkonjak Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks Electronics physically unclonable functions hardware security internet of things reconfigurable evolution strategies |
| title | Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks |
| title_full | Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks |
| title_fullStr | Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks |
| title_full_unstemmed | Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks |
| title_short | Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks |
| title_sort | evolution strategies driven optimization on secure and reconfigurable interconnection puf networks |
| topic | physically unclonable functions hardware security internet of things reconfigurable evolution strategies |
| url | https://www.mdpi.com/2079-9292/10/5/537 |
| work_keys_str_mv | AT hongxianggu evolutionstrategiesdrivenoptimizationonsecureandreconfigurableinterconnectionpufnetworks AT miodragpotkonjak evolutionstrategiesdrivenoptimizationonsecureandreconfigurableinterconnectionpufnetworks |