Invited Talks
On the Applications of Evolutionary Algorithms to the Cybersecurity Domain
Stjepan Picek, Radboud University, the Netherlands
Evolutionary algorithms (EAs) are successfully applied in many application domains. One of those domains is cybersecurity. This talk will cover several cybersecurity applications where evolutionary algorithms showed competitive performance. First, we will discuss evolutionary algorithms and their applications for cryptography (evolution of cryptographic primitives and side-channel analysis). Afterward, we will cover EAs applications for intrusion detection, modeling attacks of Physically Unclonable Functions, fuzzing, and adversarial machine learning. Finally, we will conclude this talk with a brief overview of common challenges and possible future research directions.
Stjepan Picek is an associate professor at Radboud University, Nijmegen, The Netherlands. His research interests are security/cryptography, machine learning, and evolutionary computation. Before the associate professor position, Stjepan was assistant professor at TU Delft, The Netherlands, a postdoctoral researcher at MIT, USA, and a postdoctoral researcher at KU Leuven, Belgium. Stjepan finished his PhD in 2015 with a topic on cryptology and evolutionary computation techniques. Stjepan also has several years of experience working in industry and government. Up to now, he gave more than 20 invited talks at conferences and summer schools and published more than 100 refereed papers in both evolutionary computation and cryptography journals and conferences. Stjepan is a member of the organization committee for International Summer School in Cryptography and a general co-chair for Eurocrypt 2021.
Building the Building Blocks - Evolving Hyper-Features for Model Robustness and Readability
Sara Silva, University of Lisbon, Portugal
Among its many competences, Genetic Programming (GP) can also be regarded as a feature discovery method. M3GP is a GP variant originally developed for performing multiclass classification, that recently proved to be also a powerful method for evolving hyper-features from the original data, for both classification and regression, and for a variety of machine learning methods. This talk will address how the evolved hyper-features can improve the robustness and readability of data models, with examples from remote sensing applications.
Sara Silva is a Principal Investigator of the LASIGE research center at the Faculty of Sciences of the University of Lisbon, Portugal. Her research interests are mainly in machine learning with a strong emphasis in Genetic Programming, where she has contributed with several new methods and applied them in projects related to such different domains as remote sensing, biomedicine, systems biology, maritime security, plant phenotyping, ecotoxicology and radiomics, among others. She is the author of around 100 peer-reviewed publications, and has received more than 10 nominations and awards for best paper and best researcher. In 2018 she received the EvoStar Award for Outstanding Contribution to Evolutionary Computation in Europe. She is the creator and developer of GPLAB - A Genetic Programming Toolbox for MATLAB.