16 citations · 39 across the 11 of their papers we have counts for
15 papers
Counteracting Concept Drift by Learning with Future Malware Predictions
Branislav Bosansky, Lada Hospodkova, Michal Najman +3
The accuracy of deployed malware-detection classifiers degrades over time due to changes in data distributions and increasing discrepancies between training and testing data. This…
How to Train your Antivirus: RL-based Hardening through the Problem-Space
Ilias Tsingenopoulos, Jacopo Cortellazzi, Branislav Bošanský +5
ML-based malware detection on dynamic analysis reports is vulnerable to both evasion and spurious correlations. In this work, we investigate a specific ML architecture employed in…
Cyber Deception against Zero-day Attacks: A Game Theoretic Approach
Md Abu Sayed, Ahmed H. Anwar, Christopher Kiekintveld +2
Reconnaissance activities precedent other attack steps in the cyber kill chain. Zero-day attacks exploit unknown vulnerabilities and give attackers the upper hand against conventio…
Avast-CTU Public CAPE Dataset
Branislav Bosansky, Dominik Kouba, Ondrej Manhal +4
There is a limited amount of publicly available data to support research in malware analysis technology. Particularly, there are virtually no publicly available datasets generated…
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data
Tomáš Pevný, Viliam Lisý, Branislav Bošanský +2
Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains. While m…
Computing Stackelberg Equilibrium with Memory in Sequential Games
Aditya Aradhye, Branislav Bošanský, Michael Hlaváček
Stackelberg equilibrium is a solution concept that describes optimal strategies to commit: Player 1 (the leader) first commits to a strategy that is publicly announced, then Player…