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.
Category: Master Thesis
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.