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Found 6 papers, 1 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.
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.
Computation of Stackelberg Equilibria of Finite Sequential Games
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).
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.
