Machine Learning and Evolutionary Computation in Cryptography

Abstract: In this talk, we address several applications of metaheuristics and machine learning in cryptography. We start with a brief introduction on machine learning and metaheuristic techniques and following that, we address two practical scenarios. More specifically, as the first example we consider a combinatorial optimization problem in the form of designing S-boxes with good cryptographic and implementation properties (with a special emphasis on area and power perspective). Moreover, we discuss how to use population based metaheuristic techniques like those belonging to the evolutionary computation area in the design process. Following that, we investigate how to use machine learning in side-channel attacks. There, we elaborate on the life-cycle of a machine learning process with an example application taken from the DPA contest web site, more specifically, attacking masked AES implementation. Finally, we give a brief conclusion and address several possible future research a! venues. Speaker Bio: Stjepan Picek is a postdoctoral researcher in the Computer Security and Industrial Cryptography (COSIC) group at KU Leuven, Belgium. His research interests are applied cryptography, machine learning, and evolutionary computation. He finished his PhD in 2015 as a double doctorate under the supervision of Lejla Batina (Radboud University Nijmegen, The Netherlands) and Domagoj Jakobovic (Faculty of Electrical Engineering and Computing, Croatia). Prior to that, Stjepan worked in industry and government. He regularly publishes papers in both evolutionary computation and cryptography conferences. Stjepan is also a member of organization committee for International Summer School in Cryptography and the vice-president of the Croatian IEEE CIS Chapter, as well as the member of several professional societies (ACM, IEEE, IACR).

More information at: https://www.dal.ca/news/events/2016/12/06/machine_learning_and_evolutionary_computation_in_cryptography_.html