For decades, a tremendous amount of research is being devoted to the design of efficient algorithms to solve the Boolean satisfiability problem (SAT). In practice, modern SAT solvers are able to solve a variety of difficult problems with surprising performances, including cryptanalysis-related problems. The purpose of this thesis is to investigate strategies for tuning SAT solvers to the cryptanalysis of a family of symmetric-key functions in order to gain insight into what makes these functions difficult to break.
Category: Master Thesis
Automatic synthesis methods, developed by the formal methods community, are based on different extensions of game theory and aim to produce algorithms and tools that automatically write (synthesize) pieces of code that comply with certainty with a given specification. These methods have mainly been applied to safely synthesize key elements of critical systems, where no failures are tolerated. The objective of this thesis is to explore the opportunity to apply these same techniques to synthesize cryptographic protocols such as fair-exchange protocols and/or key exchange protocols.
Presented recently, Meltdown and Spectre represent two critical vulnerabilities in modern processors. The adversaries can exploit these vulnerabilities in order to recover sensitive information stored in memory. The rationale is that these vulnerabilities allow adversaries to bypass the isolation between different applications. In this work, the student will present, execute and analyse these vulnerabilities (on several CPUs) in order to report their impact on real products.
Side-Channel Attacks are attacks against implementations of cryptographic algorithms. These attacks exploit physical properties of a device under attack. For example an attacker can measure the execution time or power consumption of a device while it executes a cryptographic algorithm.
Based on neural network, deep learning represents an active research in machine learning that allows producing automatic attacks requiring no a priori information on the underlying phenomenon. The purpose of this work is to shed new light on the capabilities of deep learning in side-channel attacks.