Name
Abstract | ||
|---|---|---|
The physical unclonable functions (PUFs) have been attracted attention to prevent semiconductor counterfeits. However, the risk of machine learning attack for an arbiter PUF, which is one of the typical PUFs, has been reported. Therefore, an XOR arbiter PUF, which has a resistance against the machine learning attack, was proposed. However, in recent years, a new machine learning attack using power consumption during the operation of the PUF circuit was reported. Also, it is important that the detailed tamper resistance verification of the PUFs to consider the security of the PUFs in the future. Therefore, this study proposes a new machine learning attack using electromagnetic waveforms for the XOR arbiter PUF. Experiments by an actual device evaluate the validity of the proposed method and the security of the XOR arbiter PUF. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3184066.3184100 | 2ND INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND SOFT COMPUTING (ICMLSC 2018) |
Keywords | Field | DocType |
Machine learning attack, electromagnetic analysis, hardware security, physical unclonable function, XOR arbiter PUF | Arbiter,Hardware security module,Computer science,Artificial intelligence,Physical unclonable function,Tamper resistance,Machine learning,Power consumption | Conference |
Citations | PageRank | References |
1 | 0.40 | 7 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nozaki, Y. | 1 | 5 | 11.62 |