چكيده به لاتين
Taking advantage of process variation during chip production, Physical Unclonable Function (PUF) can produce chips that are each slightly different from the other, and this difference can act like its fingerprint and distinguish that PUF from other PUFs. This unique behaviour, however, also presents challenges, including a modelling attack, which utilizes machine learning tools to gain insight into these differences and then models the PUF based on this information. In response to this, research has been conducted in the field of hardware security on the development of reliable and secure PUFs against these attacks.
Due to the failure of other PUFs against modeling attacks, the need for a secure PUF in hardware security is evident. Therefore, this dissertation introduces Obfuscated PUF (OPUF),
which is the most promising PUF in terms of its robustness against machine learning attacks. Since OPUF is generated using the most effective obfuscation methods, modeling attacks will be unable to model it easily. In addition to being resistant to other existing attacks, OPUF is also resistant to reliability attacks. Later, we analyze its architecture and design, and finds that OPUF is secure against recently reported machine learning attacks. In addition, it adds only a tiny amount of area overhead to the chip, making it suitable for edge devices. The simulation results for the LR and MLP attacks show that, on average, these attacks have an accuracy of 50.69% and 49.54%, respectively, which is the least accurate result of these attacks on PUFs compared to those presented prior to OPUF.
