Abstract
Aiming to find an ultimate solution to the problem of secure storage and hardware authentication, Physically Unclonable Functions (PUFs) appear to be promising primitives. While arbiter PUFs utilized in cryptographic protocols are becoming one of the most popular PUF instances, their vulnerabilities to Machine Learning (ML) attacks have been observed earlier. These attacks, as cost-effective approaches, can clone the challenge-response behavior of an arbiter PUF by collecting a subset of challenge-response pairs (CRPs). As a countermeasure against this type of attacks, PUF manufacturers shifted their focus to non-linear architectures, such as XOR arbiter PUFs with a large number of arbiter PUF chains. However, the natural question arises whether an XOR arbiter PUF with an arbitrarily large number of parallel arbiter chains can be considered secure. On the other hand, even if a mature ML approach with a significantly high accuracy is adopted, the eventual delivery of a model for an XOR arbiter PUF should be ensured. To address these issues, this paper presents a respective PAC learning framework. Regarding our framework, we are able to establish a theoretical limit on the number of arbiter chains, where an XOR arbiter PUF can be learned in polynomial time, with given levels of accuracy and confidence. In addition, we state how an XOR arbiter PUF with noisy responses can be provably PAC learned. Finally, on the basis of learning theory concepts, we conclude that no secure XOR arbiter PUF relying on current IC technologies can be manufactured.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Angluin, D., Laird, P.: Learning from noisy examples. Mach. Learn. 2(4), 343–370 (1988)
Anthony, M.: Computational Learning Theory. Cambridge University Press, Cambridge (1997)
Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Learnability and the Vapnik-Chervonenkis dimension. J. ACM 36(4), 929–965 (1989)
Bylander, T.: Learning linear threshold functions in the presence of classification noise. In: Proceedings of the Seventh Annual Conference on Computational Learning Theory, pp. 340–347 (1994)
Delvaux, J., Gu, D., Schellekens, D., Verbauwhede, I.: Secure lightweight entity authentication with strong PUFs: mission impossible? In: Batina, L., Robshaw, M. (eds.) CHES 2014. LNCS, vol. 8731, pp. 451–475. Springer, Heidelberg (2014)
Freund, Y., Schapire, R.E.: Large margin classification using the perceptron algorithm. Mach. Learn. 37(3), 277–296 (1999)
Ganji, F., Tajik, S., Seifert, J.P.: PAC Learning of Arbiter PUFs, Security Proofs for Embedded Systems-PROOFS (2014). https://eprint.iacr.org/2015/378.pdf. Accessed 18 May 2015
Gassend, B., Clarke, D., Van Dijk, M., Devadas, S.: Silicon physical random functions. In: Proceedings of the 9th ACM Conference on Computer and Communications Security, pp. 148–160 (2002)
Gassend, B., Lim, D., Clarke, D., Van Dijk, M., Devadas, S.: Identification and authentication of integrated circuits. Concurrency Comput. Pract. Experience 16(11), 1077–1098 (2004)
Hammouri, G., Öztürk, E., Sunar, B.: A tamper-proof and lightweight authentication scheme. Pervasive Mobile Comput. 4(6), 807–818 (2008)
Khardon, R., Wachman, G.: Noise tolerant variants of the perceptron algorithm. Journal Mach. Learn. Res. 8, 227–248 (2007)
Kömmerling, O., Kuhn, M.: Design principles for tamper-resistant security processors. In: USENIX Workshop on Smartcard Technology (1999)
Lee, J.W., Lim, D., Gassend, B., Suh, G.E., Van Dijk, M., Devadas, S.: A technique to build a secret key in integrated circuits for identification and authentication applications. In: Symposium on VLSI Circuits, 2004. Digest of Technical Papers, pp. 176–179 (2004)
Littlestone, N.: Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Mach. Learn. 2(4), 285–318 (1988)
Littlestone, N.: From on-line to batch learning. In: Proceedings of the Second Annual Workshop on Computational Learning Theory, pp. 269–284 (1989)
Maes, R.: Physically Unclonable Functions: Constructions, Properties and Applications. Springer, Heidelberg (2013)
Maes, R., Verbauwhede, I.: Physically unclonable functions a study on the state of the art and future research directions. In: Sadeghi, A.-R., Naccache, D. (eds.) Towards Hardware-Intrinsic Security. Information Security and Cryptography, pp. 3–37. Springer, Heidelberg (2010)
Majzoobi, M., Koushanfar, F., Devadas, S.: FPGA PUF using programmable delay lines. In: 2010 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2010)
Majzoobi, M., Koushanfar, F., Potkonjak, M.: Lightweight secure PUFs. In: Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design, pp. 670–673 (2008)
Pappu, R., Recht, B., Taylor, J., Gershenfeld, N.: Physical one-way functions. Science 297(5589), 2026–2030 (2002)
Rostami, M., Majzoobi, M., Koushanfar, F., Wallach, D., Devadas, S.: Robust and reverse-engineering resilient puf authentication and key-exchange by substring matching. IEEE Trans. Emerg. Top. Comput. 2(1), 37–49 (2014)
Ruhrmair, U., Solter, J., Sehnke, F., Xu, X., Mahmoud, A., Stoyanova, V., Dror, G., Schmidhuber, J., Burleson, W., Devadas, S.: PUF modeling attacks on simulated and silicon data. IEEE Trans. Inf. Forensics Secur. 8(11), 1876–1891 (2013)
Rührmair, U., Sehnke, F., Sölter, J., Dror, G., Devadas, S., Schmidhuber, J.: Modeling attacks on physical unclonable functions. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, pp. 237–249 (2010)
Rührmair, U., Xu, X., Sölter, J., Mahmoud, A., Majzoobi, M., Koushanfar, F., Burleson, W.: Efficient power and timing side channels for physical unclonable functions. In: Batina, L., Robshaw, M. (eds.) CHES 2014. LNCS, vol. 8731, pp. 476–492. Springer, Heidelberg (2014)
Sadeghi, A.R., Naccache, D. (eds.): Towards Hardware-Intrinsic Security: Foundations and Practice, 1st edn. Springer, Heidelberg (2010)
Servedio, R.A.: Efficient Algorithms in Computational Learning Theory. Harvard University, Cambridge (2001)
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2014)
Suh, G.E., Devadas, S.: Physical unclonable functions for device authentication and secret key generation. In: Proceedings of the 44th Annual Design Automation Conference, pp. 9–14 (2007)
Tajik, S., Dietz, E., Frohmann, S., Seifert, J.-P., Nedospasov, D., Helfmeier, C., Boit, C., Dittrich, H.: Physical characterization of arbiter PUFs. In: Batina, L., Robshaw, M. (eds.) CHES 2014. LNCS, vol. 8731, pp. 493–509. Springer, Heidelberg (2014)
Tobisch, J., Becker, G.T.: On the Scaling of Machine Learning Attacks on PUFs with Application to Noise Bifurcation (2015). https://www.emsec.rub.de/research/publications/ScalingPUFCameraReady/. Accessed 18 May 2015
Yu, M.D.M., Verbauwhede, I., Devadas, S., MRaihi, D.: A noise bifurcation architecture for linear additive physical functions. In: 2014 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), pp. 124–129 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ganji, F., Tajik, S., Seifert, JP. (2015). Why Attackers Win: On the Learnability of XOR Arbiter PUFs. In: Conti, M., Schunter, M., Askoxylakis, I. (eds) Trust and Trustworthy Computing. Trust 2015. Lecture Notes in Computer Science(), vol 9229. Springer, Cham. https://doi.org/10.1007/978-3-319-22846-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-22846-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22845-7
Online ISBN: 978-3-319-22846-4
eBook Packages: Computer ScienceComputer Science (R0)