A method of generating mutated Windows malware to evade ensemble learning-based detectors

In this study, we propose a method for generating mutated Windows malware against malware detection based on ensemble learning by combining GANs and RL to overcome the limitations of the MalGAN model.
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A method of generating mutated Windows malware to evade ensemble learning-based detectors