| Title | Deep Learning Based Attack for AI Oriented Authentication Module |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Takemoto, Shu, Shibagaki, Kazuya, Nozaki, Yusuke, Yoshikawa, Masaya |
| Conference Name | 2020 35th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) |
| Date Published | jul |
| Keywords | Artificial neural networks, authentication, Data models, Deep Learning, hardware security, Human Behavior, human factors, machine learning, machine learning attack, Metrics, physical unclonable function, pubcrawl, Scalability, Tamper resistance |
| Abstract | Neural Network Physical Unclonable Function (NN-PUF) has been proposed for the secure implementation of Edge AI. This study evaluates the tamper resistance of NN-PUF against machine learning attacks. The machine learning attack in this study learns CPRs using deep learning. As a result of the evaluation experiment, the machine learning attack predicted about 82% for CRPs. Therefore, this study revealed that NN-PUF is vulnerable to machine learning attacks. |
| Citation Key | takemoto_deep_2020 |
Deep Learning Based Attack for AI Oriented Authentication Module