Abstract
Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.
Highlights
As of today, the amount of private information stored and flows between electronic devices is unimaginable
Because we intend to prove that an interconnected PUF network (IPN) structure itself is more resilient against machine learning attacks, which means that it is much harder to predict using a machine learning model, we provided all Physical Unclonable Functions (PUFs)-based systems discussed with the same number of challenge-response pairs as well as same run-time/iterations
We have carefully studied an interconnected PUF network structure that connects PUFs to build a network in this paper
Summary
The amount of private information stored and flows between electronic devices is unimaginable. Adversaries are highly motivated to attack these electronics because of the potential benefits that they can gain from the stolen personal information. Physical Unclonable Functions (PUFs) came to the stage when traditional cryptography failed to stand its ground against physical attacks, side-channel attacks, and API attacks. A PUF, different from traditional key-based cryptographic systems, does not require a secret binary key; instead, the physical entity itself serves as the key. One huge advantage of a PUF-based system is that the secret key that is hidden within the physical body is designed to be unclonable, since it utilizes uncontrollable, nanoscale process variations. The complex structure of a PUF makes the output much harder to be predicted or derived comparing to those digital systems that stores secret keys in non-volatile memories
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