A Deep Learning Attack Countermeasure with Intentional Noise for a PUF-Based Authentication Scheme

We propose a scheme to prevent the machine learning (ML) attacks against physically unclonable functions (PUFs). A silicon PUF is a security primitive in a semiconductor chip that generates a unique identifier by exploiting device variations. However, some PUF implementations are vulnerable to ML attacks, in which an attacker tries to obtain the mathematical clone of the target PUF to predict its responses. Our scheme adds intentional noise to the responses to disturb ML by an attacker so that the clone fails to be authenticated, while the original PUF can still be correctly authenticated using an error correction code (ECC). The effectiveness of this scheme is not very obvious because the attacker can also use the ECC. We apply the countermeasure to n-XOR arbiter PUFs to investigate the feasibility of the proposed scheme. We explain the relationship between the prediction accuracy of the clone and the number of intentional noise bits. Our scheme can successfully distinguish a clone from the legitimate PUF in the case of 5-XOR PUF.

[1]  Rajat Subhra Chakraborty,et al.  Deep Learning based Model Building Attacks on Arbiter PUF Compositions , 2019, IACR Cryptol. ePrint Arch..

[2]  Ingrid Verbauwhede,et al.  PUFKY: A Fully Functional PUF-Based Cryptographic Key Generator , 2012, CHES.

[3]  Yuejiang Wen,et al.  Improving Security and Reliability of Physical Unclonable Functions Using Machine Learning , 2018 .

[4]  Srinivas Devadas,et al.  Silicon physical random functions , 2002, CCS '02.

[5]  Stefan Katzenbeisser,et al.  Reverse Fuzzy Extractors: Enabling Lightweight Mutual Authentication for PUF-Enabled RFIDs , 2012, Financial Cryptography.

[6]  Makoto Ikeda,et al.  PUFNet: A Deep Neural Network Based Modeling Attack for Physically Unclonable Function , 2019, 2019 IEEE International Symposium on Circuits and Systems (ISCAS).

[7]  G. Edward Suh,et al.  Physical Unclonable Functions for Device Authentication and Secret Key Generation , 2007, 2007 44th ACM/IEEE Design Automation Conference.

[8]  Kazukuni Kobara,et al.  Evaluation of Physical Unclonable Functions for 28-nm Process Field-Programmable Gate Arrays , 2014, J. Inf. Process..

[9]  Alan Zimmerman,et al.  Protecting Your Intellectual Property Rights , 2013 .

[10]  G. Edward Suh,et al.  Extracting secret keys from integrated circuits , 2005, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[11]  Jeroen Delvaux,et al.  Side channel modeling attacks on 65nm arbiter PUFs exploiting CMOS device noise , 2013, 2013 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST).

[12]  Stephen A. Benton,et al.  Physical one-way functions , 2001 .

[13]  Srinivas Devadas,et al.  FPGA Implementation of a Cryptographically-Secure PUF Based on Learning Parity with Noise , 2017, Cryptogr..

[14]  Mitsugu Iwamoto,et al.  A New Arbiter PUF for Enhancing Unpredictability on FPGA , 2015, TheScientificWorldJournal.

[15]  Ingrid Verbauwhede,et al.  Low-Overhead Implementation of a Soft Decision Helper Data Algorithm for SRAM PUFs , 2009, CHES.

[16]  Yevgeniy Dodis,et al.  Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data , 2004, EUROCRYPT.

[17]  Srinivas Devadas,et al.  Modeling attacks on physical unclonable functions , 2010, CCS '10.

[18]  Srinivas Devadas,et al.  Trapdoor Computational Fuzzy Extractors and Stateless Cryptographically-Secure Physical Unclonable Functions , 2017, IEEE Transactions on Dependable and Secure Computing.

[19]  Mahmoud Khalafalla,et al.  PUFs Deep Attacks: Enhanced modeling attacks using deep learning techniques to break the security of double arbiter PUFs , 2019, 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[20]  Mitsugu Iwamoto,et al.  Deep-Learning-Based Security Evaluation on Authentication Systems Using Arbiter PUF and Its Variants , 2016, IWSEC.

[21]  Georg T. Becker,et al.  The Gap Between Promise and Reality: On the Insecurity of XOR Arbiter PUFs , 2015, CHES.