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Intro; Foreword; Preface; Acknowledgments; Contents; Contributors; Acronyms; 1 Introduction to Fault Analysis in Cryptography; 1.1 Cryptography Background; 1.2 Fault Injection Attacks; 1.3 Fault Attack Methods; 1.4 Fault Injection Techniques; 1.5 Countermeasures; 1.6 Chapter Summary; References; Part I Automated Fault Analysis of Symmetric Block Ciphers; 2 ExpFault: An Automated Framework for Block CipherFault Analysis; 2.1 Introduction; 2.2 Preliminaries; 2.2.1 Basic Terminology; 2.2.2 AES; 2.2.3 PRESENT; 2.3 Automated Fault Analysis: A Brief Discussion; 2.3.1 Some Early Automation Efforts
2.3.2 ExpFault: An Overview2.4 A Formalization of the Differential Fault Analysis; 2.4.1 DFA on Block Ciphers: A Generic View; 2.4.2 Representing a Block Cipher; 2.4.3 Formalization of the DFA; 2.5 A Framework for Exploitable Fault Characterization; 2.5.1 Automatic Identification of Distinguishers; 2.5.1.1 Case 1: The Variables Are Independent, But Not Uniform Within the Complete Range; 2.5.1.2 Case 2: The Variables Are Not Independent; 2.5.2 Enabling Divide-and-Conquer in Distinguisher Enumeration Algorithm T; 2.5.3 Complexity Evaluation of the Remaining Keyspace R; 2.6 Case Studies
2.6.1 Differential Fault Attack on GIFT Block Cipher2.7 Chapter Summary; Appendix 1: Implementation Details of ExpFault; Assumptions; Inputs and Outputs; Setup for Distinguisher Identification; Analysis of Runtime; Current Limitations; Appendix 2: More on the DFA of GIFT; Calculation of the Attack Complexity; Other Attacks on GIFT; Appendix 3: Comparison with the AFA and ML-AFA; References; 3 Exploitable Fault Space Characterization: A Complementary Approach; 3.1 Introduction; 3.2 Preliminaries; 3.2.1 General Model for Block Cipher and Faults; 3.2.2 Algebraic Representation of Ciphers
3.3 ML-Based Fault Space Characterization: Methodology3.3.1 Motivation; 3.3.2 Empirical Hardness Prediction of Satisfiability Problems; 3.3.3 ML Model for Exploitable Fault Identifier; 3.3.4 Feature Set Description; 3.3.5 Handling the False Negatives; 3.4 Case Studies; 3.4.1 Learning Exploitable Faults for PRESENT; 3.4.1.1 Experimental Setup; 3.4.1.2 Feature Selection; 3.4.1.3 Classification; 3.4.1.4 Handling False Negatives; 3.4.1.5 Gain over Exhaustive SAT Solving; 3.4.1.6 Discussion; 3.4.2 Exploitable Fault Space Characterization for LED; 3.4.2.1 ML Experiments
3.4.2.2 Discovery of New Attacks3.4.2.3 Discussion; 3.4.3 Utilizing the Success Rate: Analyzing the Effect of S-Boxes on Fault Attacks; 3.4.3.1 Analysis of the Observations; 3.4.3.2 Discussion; 3.5 Chapter Summary; References; 4 Differential Fault Analysis Automation on Assembly Code; 4.1 Introduction; 4.2 Preliminaries; 4.2.1 Related Work; 4.2.2 Intermediate Representation; 4.2.3 Assumptions and Scope; 4.2.4 Formalization of Fault Attack; 4.3 Automated Assembly Code Analysis; 4.3.1 Overview; 4.3.2 From Assembly to Data Flow Graph; 4.3.3 Output Criteria
2.3.2 ExpFault: An Overview2.4 A Formalization of the Differential Fault Analysis; 2.4.1 DFA on Block Ciphers: A Generic View; 2.4.2 Representing a Block Cipher; 2.4.3 Formalization of the DFA; 2.5 A Framework for Exploitable Fault Characterization; 2.5.1 Automatic Identification of Distinguishers; 2.5.1.1 Case 1: The Variables Are Independent, But Not Uniform Within the Complete Range; 2.5.1.2 Case 2: The Variables Are Not Independent; 2.5.2 Enabling Divide-and-Conquer in Distinguisher Enumeration Algorithm T; 2.5.3 Complexity Evaluation of the Remaining Keyspace R; 2.6 Case Studies
2.6.1 Differential Fault Attack on GIFT Block Cipher2.7 Chapter Summary; Appendix 1: Implementation Details of ExpFault; Assumptions; Inputs and Outputs; Setup for Distinguisher Identification; Analysis of Runtime; Current Limitations; Appendix 2: More on the DFA of GIFT; Calculation of the Attack Complexity; Other Attacks on GIFT; Appendix 3: Comparison with the AFA and ML-AFA; References; 3 Exploitable Fault Space Characterization: A Complementary Approach; 3.1 Introduction; 3.2 Preliminaries; 3.2.1 General Model for Block Cipher and Faults; 3.2.2 Algebraic Representation of Ciphers
3.3 ML-Based Fault Space Characterization: Methodology3.3.1 Motivation; 3.3.2 Empirical Hardness Prediction of Satisfiability Problems; 3.3.3 ML Model for Exploitable Fault Identifier; 3.3.4 Feature Set Description; 3.3.5 Handling the False Negatives; 3.4 Case Studies; 3.4.1 Learning Exploitable Faults for PRESENT; 3.4.1.1 Experimental Setup; 3.4.1.2 Feature Selection; 3.4.1.3 Classification; 3.4.1.4 Handling False Negatives; 3.4.1.5 Gain over Exhaustive SAT Solving; 3.4.1.6 Discussion; 3.4.2 Exploitable Fault Space Characterization for LED; 3.4.2.1 ML Experiments
3.4.2.2 Discovery of New Attacks3.4.2.3 Discussion; 3.4.3 Utilizing the Success Rate: Analyzing the Effect of S-Boxes on Fault Attacks; 3.4.3.1 Analysis of the Observations; 3.4.3.2 Discussion; 3.5 Chapter Summary; References; 4 Differential Fault Analysis Automation on Assembly Code; 4.1 Introduction; 4.2 Preliminaries; 4.2.1 Related Work; 4.2.2 Intermediate Representation; 4.2.3 Assumptions and Scope; 4.2.4 Formalization of Fault Attack; 4.3 Automated Assembly Code Analysis; 4.3.1 Overview; 4.3.2 From Assembly to Data Flow Graph; 4.3.3 Output Criteria