Realizing strong PUF from weak PUF via neural computing

Physically Unclonable Functions (PUFs) are hardware-based security primitives that promise to provide an advantage in terms of area and power compared to hardware implementations of standard cryptography algorithms. PUFs harness manufacturing process variations to realize binary keys (Weak PUFs) or binary functions (SStrong PUFs). An ideal Strong PUF realizes a binary function that maps an m-bit input challenge to a random n-bit output response and offers an exponential number of such unique challenge-response pairs (CRPs). Hence, it is attractive for authentication applications. Unfortunately, most Strong PUF implementations are non-ideal, where an adversary can build a machine-learning model by observing a relatively few CRPs, making it possible to predict the output response of a PUF to a future challenge. Existence of such a model, or clone, constitutes a breach of security. In this paper, we make two contributions: first, we demonstrate that by leveraging a Weightless Neural Network (WNN), we can realize a CMOS Strong PUF from a Weak PUF. Next, we demonstrate that WNN based Strong PUFs offer robust resistance to machine-learning, while also delivering on uniqueness and reliability metrics — bringing it closer to an ideal Strong PUF. Neural network hardware is gaining importance for pattern matching and classification. This work demonstrates how such a design may be re-purposed for security. In the rest of the paper, we present architecture, practical implementation and analysis of Neural Network based PUFs.