PUF optimization for IoT device authentication

Two critical aspects in IoT are security and resource consumption. Physically Unclonable Functions (PUFs) are hardware security primitives that exploit the inherent manufacturing variations of integrated circuits to generate unique identifiers. Due to their uniqueness and resistance to cloning, they are widely used for IoT device authentication. In this work, a novel approach to enhance the identifiability of compensated measurement PUFs is introduced. This method involves applying specific weight masks to the parameters extracted from the PUF entropy source before conducting comparisons to determine the output bit. This technique has been tested on several types of PUFs constructed using public datasets. As a result, it has been observed that the Equal Error Rate ( EER ) can be greatly improved, up to two orders of magnitude. The main advantage of this technique is that it does not modify the architecture of the compensated measurement PUF (which is interesting for IoT devices, as it does not require extra resources), while it also has proven to be generalizable, i.e., the optimal mask parameters can be found using a small set of devices and, then, generalized to a different bigger set of devices. This way, this proposal contributes to the development of novel authentication schemes for IoT devices, addressing both security and resource efficiency, critical issues in IoT environments. • A novel optimization technique to improve the identifiability of a compensated measurement PUF and its theoretical justification. • The optimization requires only a small subset of devices as a training dataset, enabling generalization to a larger population of devices. • The implementation of this technique and test using different types of PUFs constructed using public databases. • The implementation of one technique which does not require additional hardware resources based on initializing counters to a certain value.