CRC-PUF: A Machine Learning Attack Resistant Lightweight PUF Construction

Abstract

Adversarial machine learning is an emerging threat to security of Machine Learning (ML)-based systems. However, we can potentially use it as a weapon against ML-based attacks. In this paper, we focus on protecting Physical Unclonable Functions (PUFs) against ML-based modeling attacks. PUFs are an important cryptographic primitive for secret key generation and challenge-response authentication. However, none of the existing PUF constructions are both ML attack resistant and sufficiently lightweight to fit low-end embedded devices. We present a lightweight PUF construction, CRC-PUF, in which input challenges are de-synchronized from output responses to make a PUF model difficult to learn. The de-synchronization is done by an input transformation based on a Cyclic Redundancy Check (CRC). By changing the CRC generator polynomial for each new response, we assure that success probability of recovering the transformed challenge is at most 2 −86 for 128-bit challenges and responses.

MARPUF: physical unclonable function with improved machine learning attack resistance

“…The cyclic redundancy check PUF (CRC PUF) design discussed in [12] consists of one n ‐bit LFSR and one n ‐bit arbiter PUF. The total GE for 128‐bit CRC PUF is calculated by considering one 128‐bit LFSR apart from basic 128‐bit arbiter PUF.…”

“…In other words, the MARPUF is more resistant to ML attacks. We also compared the proposed MARPUF with other PUF schemes like CRC PUF [12], interpose PUF [13] , and Wang et al PUF [16] to evaluate the hardware cost and it is observed from Table 3 that the proposed MARPUF has the edge over the said PUF designs. Furthermore, MARPUF requires two clock cycles to generate the output as PUF response, which is higher than the conventional PUF like RO PUF and arbiter PUF designs, as MARPUF involves two rounds of operation.…”

“…The entropy for two round MARPUF is calculated as 0.928, whereas the entropy for the usual RO PUF is calculated as 0.656. Furthermore, we have also calculated MinEntropy, which is defined in Equation (12). MinEntropy for the proposed MARPUF is found to be 0.692, while for RO PUF, it is 0.613.…”

“…The modelling attack accuracy for 16-bit, 32-bit, and 64-bit MARPUF is also studied along with 128-bit PUF size and compared with some of the existing PUFs. The comparison result is shown in The cyclic redundancy check PUF (CRC PUF) design discussed in [12] consists of one n-bit LFSR and one n-bit arbiter PUF. The total GE for 128-bit CRC PUF is calculated by considering one 128-bit LFSR apart from basic 128-bit arbiter PUF.…”

Puf-G

“…This has also surfaced non-invasive ML attack vulnerabilities to strong composite PUF models and lightweight PUF models [35]. Consequently, sound mathematical representations and provable guarantees are studied to theoretically justify the PAC-learnability for a family of PUFs [6], which has further led to the exploratory development in the field of ML-resistant PUF designs [4,14,15,41].…”