| Title | Tamper Resistance Evaluation of PUF Implementation Against Machine Learning Attack |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Nozaki, Yusuke, Yoshikawa, Masaya |
| Conference Name | Proceedings of the 2017 International Conference on Biometrics Engineering and Application |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4871-3 |
| Keywords | authentication of semiconductor component, Human Behavior, machine learning, Metrics, physical unclonable function, pubcrawl, Scalability, Tamper resistance |
| Abstract | Recently, the semiconductor counterfeiting has become a serious problem. To counter this problem, Physical Unclonable Function (PUF) has been attracted attention. However, the risk of machine learning attacks for PUF is pointed out. To verify the safety of PUF, the evaluation (tamper resistance) against machine learning attacks in the difference of PUF implementations is very important. However, the tamper resistance evaluation in the difference of PUF implementation has barely been reported. Therefore, this study evaluates the tamper resistance of PUF in the difference of field programmable gate array (FPGA) implementations against machine learning attacks. Experiments using an FPGA clarified the arbiter PUF of the lookup table implementation has the tamper resistance against machine learning attacks. |
| URL | http://doi.acm.org/10.1145/3077829.3077830 |
| DOI | 10.1145/3077829.3077830 |
| Citation Key | nozaki_tamper_2017 |
Tamper Resistance Evaluation of PUF Implementation Against Machine Learning Attack