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Found 6 papers, 4 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.
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
However, machine learning models are susceptible to adversarial attacks, requiring the testing of model and product robustness.
Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments
The environment is designed to eventually support complex multi-agent scenarios within the network security domain.
Ranked #1 on
CyberBattleSim
on CyberBattleSim chain scenario
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions
We achieved good surrogates of the stand-alone classifiers with up to 99\% agreement with the target models, using less than 4% of the original training dataset.
A Survey of Privacy Attacks in Machine Learning
Our contribution in this research is an analysis of more than 40 papers related to privacy attacks against machine learning that have been published during the past seven years.
DNS Tunneling: A Deep Learning based Lexicographical Detection Approach
Domain Name Service is a trusted protocol made for name resolution, but during past years some approaches have been developed to use it for data transfer.
