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Table of contents
Contents
1 Context, Objectives and Contributions
1.1 Frame of the Thesis
1.2 From the Attacks towards the Evaluation
1.3 Deep Learning based Attacks
1.4 Contributions of this Thesis
2 Preliminaries
2.1 Notations and Conventions
2.2 Recalls in Probability and Statistics
2.3 Recalls in Discrete Mathematics
2.4 Recalls on AES
2.5 Recalls on Vectorial Calculus
3 Side-Channel Attacks
3.1 Definition of a Side-Channel Attack
3.2 Assessing an Attack
3.3 Conditions of an Optimal Attack
3.4 Profiled Attacks
3.5 Unprofiled Attacks
3.6 Leakage Characterization and Pre-Processing
3.7 Counter-Measures
3.8 Overview of the Used Datasets
3.9 Conclusion
4 Deep Learning for Side-Channel Analysis
4.1 The Statistical Learning Theory
4.2 The Neural Networks Class Hypothesis
4.3 Implementing the ERM with Neural Networks
4.4 An Overview of the Literature
4.5 Conclusion
5 Theoretical Aspects of Deep Learning Based Side-Channel Analysis
5.1 Introduction
5.2 Model Training for Leakage Assessment
5.3 NLL Minimization is PI Maximization
5.4 Study on Simulated Data
5.5 Application on Experimental Data
5.6 Conclusion
6 DL-based SCA on Large-Scale Traces: A Case Study on a Polymorphic AES
6.1 Introduction
6.2 Evaluation Methodology
6.3 Results .
6.4 Discussion
6.5 Conclusion
7 Gradient Visualization for General Characterization in Profiling Attack
7.1 Introduction
7.2 Study of an Optimal Model
7.3 Proposal for a Characterization Method
7.4 Experimental Verification
7.5 Results .
7.6 Conclusion
8 Conclusion & Perspectives
8.1 Summary of the Contributions
8.2 New Tracks of Research in Deep Learning (DL)-based Side-Chanel Analysis (SCA) .
A Noise Amplification of Secret-Sharing I
A.1 The Link between Noise Amplification and Convolution
A.2 A Fixed-Point-Like Proof
B List of acronyms
C Glossary
D List of symbols
List of Figures
List of Tables
