Ganji On the Learnability of Physically Unclonable Functions

1. Auflage 2018
ISBN: 978-3-319-76717-8
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 101 Seiten

Reihe: T-Labs Series in Telecommunication Services

This book addresses the issue of Machine Learning (ML) attacks on Integrated Circuits through Physical Unclonable Functions (PUFs). It provides the mathematical proofs of the vulnerability of various PUF families, including Arbiter, XOR Arbiter, ring-oscillator, and bistable ring PUFs, to ML attacks. To achieve this goal, it develops a generic framework for the assessment of these PUFs based on two main approaches. First, with regard to the inherent physical characteristics, it establishes fit-for-purpose mathematical representations of the PUFs mentioned above, which adequately reflect the physical behavior of these primitives. To this end, notions and formalizations that are already familiar to the ML theory world are reintroduced in order to give a better understanding of why, how, and to what extent ML attacks against PUFs can be feasible in practice. Second, the book explores polynomial time ML algorithms, which can learn the PUFs under the appropriate representation. More importantly, in contrast to previous ML approaches, the framework presented here ensures not only the accuracy of the model mimicking the behavior of the PUF, but also the delivery of such a model. Besides off-the-shelf ML algorithms, the book applies a set of algorithms hailing from the field of property testing, which can help to evaluate the security of PUFs. They serve as a 'toolbox', from which PUF designers and manufacturers can choose the indicators most relevant for their requirements. Last but not least, on the basis of learning theory concepts, the book explicitly states that the PUF families cannot be considered as an ultimate solution to the problem of insecure ICs. As such, it provides essential insights into both academic research on and the design and manufacturing of PUFs.

Autoren/Hrsg.

Ganji, Fatemeh

Inhaltsverzeichnis

1;List of Publications Related to this Thesis;7
2;Contents;8
3;Acronyms;10
4;Symbols and Operators;11
5;List of Figures;15
6;List of Tables;16
7;List of Algorithms;17
8;Abstract;18
9;1 Introduction;20
9.1;1.1 Motivation;20
9.1.1;1.1.1 Hardware Root of Trust;21
9.1.2;1.1.2 Fragile Security of ICs;21
9.2;1.2 Physically Unclonable Functions;22
9.3;1.3 Thesis Statement;24
9.3.1;1.3.1 Problem Statement;24
9.3.2;1.3.2 Our Attack Model;25
9.3.3;1.3.3 Thesis Contributions;26
9.4;1.4 Outline of the Thesis;27
10;2 Definitions and Preliminaries;28
10.1;2.1 Notations;28
10.2;2.2 PUFs;28
10.3;2.3 Boolean Functions;30
10.3.1;2.3.1 Linearity of Boolean Functions;31
10.3.2;2.3.2 Average Sensitivity of Boolean Functions;32
10.3.3;2.3.3 Non-linearity of PUFs over mathbbF2 and the Existence of Influential Bits;33
10.4;2.4 Linear Threshold Functions;34
10.5;2.5 Regular Language and Principles of DFAs;36
10.6;2.6 Probably Approximately Correct Model;37
11;3 PAC Learning of Arbiter PUFs;40
11.1;3.1 Introduction;40
11.2;3.2 Representing Arbiter PUFs by DFAs;42
11.2.1;3.2.1 Discretization Process of Delay Values;44
11.2.2;3.2.2 Building a DFA Representing an Arbiter PUF;45
11.3;3.3 PAC Learning of Arbiter PUFs;48
11.4;3.4 Comparison with Related Work;51
11.5;3.5 Practical Considerations;52
11.5.1;3.5.1 The Important Role of M;52
11.5.2;3.5.2 Dealing with the Metastable Condition;53
12;4 PAC Learning of XOR Arbiter PUFs;54
12.1;4.1 Introduction;55
12.2;4.2 LTF Representation of XOR Arbiter PUFs;56
12.3;4.3 PAC Learning of XOR Arbiter PUFs;58
12.4;4.4 PAC Learning of Noisy XOR Arbiter PUFs;62
12.5;4.5 Discussion;63
12.5.1;4.5.1 Theoretical Considerations;63
12.5.2;4.5.2 Practical Considerations;65
13;5 PAC Learning of Ring Oscillator PUFs;67
13.1;5.1 Introduction;68
13.2;5.2 DL Representation of RO-PUFs;69
13.3;5.3 PAC Learnability of the 2-DL Representing the RO-PUF;71
13.4;5.4 Results and Discussion;73
14;6 PAC Learning of Bistable Ring PUFs;76
14.1;6.1 Introduction;77
14.2;6.2 Architecture of the BR-PUF Family;78
14.3;6.3 A Constant Upper Bound on the Number of Influential Bits;79
14.3.1;6.3.1 Heuristic Approaches;79
14.3.2;6.3.2 A Boolean-Analytical Approach;81
14.4;6.4 PAC Learning of PUFs Without Prior Knowledge of Their …;83
14.5;6.5 Results and Discussion;85
15;7 Follow-Up Work;90
15.1;7.1 Lattice Basis Reduction Attack;90
15.2;7.2 Laser Fault Injection Attack;91
16;8 Conclusion and Future Work;93
17; References;96