A Novel Hybrid Delay Based Physical Unclonable Function Immune to Machine Learning Attacks
| Title | A Novel Hybrid Delay Based Physical Unclonable Function Immune to Machine Learning Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Pundir, N., Hazari, N. A., Amsaad, F., Niamat, M. |
| Conference Name | 2017 IEEE National Aerospace and Electronics Conference (NAECON) |
| Date Published | jun |
| Keywords | Adversary Models, Agriculture, arbiter PUF, FPGA, hardware security, Human Behavior, Humidity, Hybrid PUF, Metrics, Modeling Attacks, Monitoring, NIST, pubcrawl, resilience, Resiliency, Ring oscillators, Scalability, Temperature distribution, Temperature measurement, Temperature sensors, Wireless sensor networks |
| Abstract | In this paper, machine learning attacks are performed on a novel hybrid delay based Arbiter Ring Oscillator PUF (AROPUF). The AROPUF exhibits improved results when compared to traditional Arbiter Physical Unclonable Function (APUF). The challenge-response pairs (CRPs) from both PUFs are fed to the multilayered perceptron model (MLP) with one hidden layer. The results show that the CRPs generated from the proposed AROPUF has more training and prediction errors when compared to the APUF, thus making it more difficult for the adversary to predict the CRPs. |
| URL | http://ieeexplore.ieee.org/document/8268749/ |
| DOI | 10.1109/NAECON.2017.8268749 |
| Citation Key | pundir_novel_2017 |