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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.
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.
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