no code implementations • 14 Apr 2024 • Branislav Bosansky, Lada Hospodkova, Michal Najman, Maria Rigaki, Elnaz Babayeva, Viliam Lisy
We use GANs to learn changes in data distributions within different time periods of training data and then apply these changes to generate samples that could be in testing data.
1 code implementation • 6 Sep 2022 • Branislav Bosansky, Dominik Kouba, Ondrej Manhal, Thorsten Sick, Viliam Lisy, Jakub Kroustek, Petr Somol
The benefit of using dynamic sandboxes is the realistic simulation of file execution in the target machine and obtaining a log of such execution.
no code implementations • 22 Oct 2021 • Marek Galovic, Branislav Bosansky, Viliam Lisy
In malware behavioral analysis, the list of accessed and created files very often indicates whether the examined file is malicious or benign.
no code implementations • 22 Apr 2020 • Olga Petrova, Karel Durkota, Galina Alperovich, Karel Horak, Michal Najman, Branislav Bosansky, Viliam Lisy
Experiments show that both algorithms are applicable for cases with low feature space dimensions but the learning-based method produces less exploitable strategies and it is scalable to higher dimensions.
no code implementations • 28 Jul 2015 • Branislav Bosansky, Simina Branzei, Kristoffer Arnsfelt Hansen, Peter Bro Miltersen, Troels Bjerre Sorensen
The Stackelberg equilibrium solution concept describes optimal strategies to commit to: Player 1 (termed the leader) publicly commits to a strategy and Player 2 (termed the follower) plays a best response to this strategy (ties are broken in favor of the leader).
no code implementations • NeurIPS 2013 • Viliam Lisy, Vojta Kovarik, Marc Lanctot, Branislav Bosansky
In this paper, we study Monte Carlo tree search (MCTS) in zero-sum extensive-form games with perfect information and simultaneous moves.