Maskdga: An evasion attack against dga classifiers and adversarial defenses

Lior Sidi, Asaf Nadler, Asaf Shabtai

IEEE access 8, 161580-161592, 2020

Domain generation algorithms (DGAs) are commonly used by botnets to generate domain names that bots can use to establish communication channels with their command and control servers. Recent publications presented deep learning classifiers that detect algorithmically generated domain (AGD) names in real time with high accuracy and thus significantly reduce the effectiveness of DGAs for botnet communication. In this paper, we present MaskDGA, an evasion technique that uses adversarial learning to modify AGD names in order to evade inline DGA classifiers, without the need for the attacker to possess any knowledge about the DGA classifier’s architecture or parameters. MaskDGA was evaluated on four state-of-the-art DGA classifiers and outperformed the recently proposed CharBot and DeepDGA evasion techniques. We also evaluated MaskDGA on enhanced versions of the same classifiers …