gagan3012/DomainGAN
Paper Implementation of DomainGAN: Generating Adversarial Examples to Attack Domain Generation Algorithm Classifiers
This tool helps cybersecurity professionals evaluate the robustness of their Domain Generation Algorithm (DGA) classifiers. By inputting an existing DGA classifier, it generates new, evasive DGA examples designed to bypass detection. The output provides a set of adversarial domain names that can be used to test and strengthen a DGA detection system.
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Use this if you are a cybersecurity analyst or researcher who needs to stress-test your DGA detection systems against advanced evasion techniques.
Not ideal if you are looking for a tool to detect DGA domains in live network traffic, as this is for generating adversarial examples, not for real-time classification.
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Jupyter Notebook
License
MIT
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Last pushed
Jun 08, 2021
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