Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs
Physical Unclonable Functions (PUFs) are used for authentication and generation of secure cryptographic keys. However, recent research work has shown that PUFs, in general, are vulnerable to machine learning modeling attacks. From a subset of Challenge-Response Pairs (CRPs), the remaining CRPs can b...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9526634/ |
| _version_ | 1818602343543865344 |
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| author | Ahmed Oun Noor Ahmad Hazari Mohammed Y. Niamat |
| author_facet | Ahmed Oun Noor Ahmad Hazari Mohammed Y. Niamat |
| author_sort | Ahmed Oun |
| collection | DOAJ |
| description | Physical Unclonable Functions (PUFs) are used for authentication and generation of secure cryptographic keys. However, recent research work has shown that PUFs, in general, are vulnerable to machine learning modeling attacks. From a subset of Challenge-Response Pairs (CRPs), the remaining CRPs can be effectively predicted using different machine learning algorithms. In this work, Artificial Neural Networks (ANNs) using swarm intelligence-based modeling attacks are used against different silicon-based PUFs to test their resiliency to these attacks. Amongst the swarm intelligence algorithms, the Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Particle Swarm Optimizer (PSO) and the Grey Wolf Optimizer (GWO) are used. The attacks are extensively performed on six different types of PUFs; namely, Configurable Ring Oscillator, Inverter Ring Oscillator, XOR-Inverter Ring Oscillator, Arbiter, Modified XOR-Inverter Ring Oscillator, and Hybrid Delay Based PUF. From the results, it can be concluded that the first four PUFs under study are vulnerable to ANN swarm intelligence-based models, and their responses can be predicted with an average accuracy of 71.1% to 88.3% for the different models. However, for the Hybrid Delay Based PUF and the Modified XOR-Inverter Ring Oscillator PUF, which are especially designed to thwart machine learning attacks, the prediction accuracy is much lower and in the range of 9.8% to 14.5%. |
| first_indexed | 2024-12-16T13:05:46Z |
| format | Article |
| id | doaj.art-65bbd168ac8647c99f32a02ba8837a43 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:05:46Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-65bbd168ac8647c99f32a02ba8837a432022-12-21T22:30:45ZengIEEEIEEE Access2169-35362021-01-01912174312175810.1109/ACCESS.2021.31092359526634Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFsAhmed Oun0https://orcid.org/0000-0001-6069-9261Noor Ahmad Hazari1Mohammed Y. Niamat2https://orcid.org/0000-0002-1896-1569Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH, USADepartment of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH, USADepartment of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH, USAPhysical Unclonable Functions (PUFs) are used for authentication and generation of secure cryptographic keys. However, recent research work has shown that PUFs, in general, are vulnerable to machine learning modeling attacks. From a subset of Challenge-Response Pairs (CRPs), the remaining CRPs can be effectively predicted using different machine learning algorithms. In this work, Artificial Neural Networks (ANNs) using swarm intelligence-based modeling attacks are used against different silicon-based PUFs to test their resiliency to these attacks. Amongst the swarm intelligence algorithms, the Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Particle Swarm Optimizer (PSO) and the Grey Wolf Optimizer (GWO) are used. The attacks are extensively performed on six different types of PUFs; namely, Configurable Ring Oscillator, Inverter Ring Oscillator, XOR-Inverter Ring Oscillator, Arbiter, Modified XOR-Inverter Ring Oscillator, and Hybrid Delay Based PUF. From the results, it can be concluded that the first four PUFs under study are vulnerable to ANN swarm intelligence-based models, and their responses can be predicted with an average accuracy of 71.1% to 88.3% for the different models. However, for the Hybrid Delay Based PUF and the Modified XOR-Inverter Ring Oscillator PUF, which are especially designed to thwart machine learning attacks, the prediction accuracy is much lower and in the range of 9.8% to 14.5%.https://ieeexplore.ieee.org/document/9526634/Hardware securityFPGAPUFartificial neural networkswarm intelligenceGSA |
| spellingShingle | Ahmed Oun Noor Ahmad Hazari Mohammed Y. Niamat Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs IEEE Access Hardware security FPGA PUF artificial neural network swarm intelligence GSA |
| title | Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs |
| title_full | Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs |
| title_fullStr | Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs |
| title_full_unstemmed | Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs |
| title_short | Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs |
| title_sort | analysis of swarm intelligence based ann algorithms for attacking pufs |
| topic | Hardware security FPGA PUF artificial neural network swarm intelligence GSA |
| url | https://ieeexplore.ieee.org/document/9526634/ |
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