Search Results for author:
Found 8 papers, 3 papers with code
Counteracting Concept Drift by Learning with Future Malware Predictions
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.
Avast-CTU Public CAPE Dataset
The benefit of using dynamic sandboxes is the realistic simulation of file execution in the target machine and obtaining a log of such execution.
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations
In malware behavioral analysis, the list of accessed and created files very often indicates whether the examined file is malicious or benign.
Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data
Learning from raw data input, thus limiting the need for manual feature engineering, is one of the key components of many successful applications of machine learning methods.
Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report
In this report, we present results reproductions for several core algorithms implemented in the OpenSpiel framework for learning in games.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection
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.
Equilibrium Approximation Quality of Current No-Limit Poker Bots
Approximating a Nash equilibrium is currently the best performing approach for creating poker-playing programs.
Computer Science and Game Theory
Convergence of Monte Carlo Tree Search in Simultaneous Move Games
In this paper, we study Monte Carlo tree search (MCTS) in zero-sum extensive-form games with perfect information and simultaneous moves.
