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Domain generation algorithms (DGAs) are commonly leveraged by malware tocreate lists of domain names which can be used for command and control (C&C)purposes. Approaches based on machine learning have recently been developed toautomatically detect generated domain names in real-time. In this work, wepresent a novel DGA called CharBot which is capable of producing large numbersof unregistered domain names that are not detected by state-of-the-artclassifiers for real-time detection of DGAs, including the recently publishedmethods FANCI (a random forest based on human-engineered features) and LSTM.MI(a deep learning approach). CharBot is very simple, effective and requires noknowledge of the targeted DGA classifiers. We show that retraining theclassifiers on CharBot samples is not a viable defense strategy. We believethese findings show that DGA classifiers are inherently vulnerable toadversarial attacks if they rely only on the domain name string to make adecision. Designing a robust DGA classifier may, therefore, necessitate the useof additional information besides the domain name alone. To the best of ourknowledge, CharBot is the simplest and most efficient black-box adversarialattack against DGA classifiers proposed to date.

CharBot: A Simple and Effective Method for Evading DGA Classifiers