CLETer: A Character-level Evasion Technique Against Deep Learning DGA Classifiers

Abstract

The detection of pseudo-random domain names generated by Domain Generation Algorithms (DGAs) is one of the effective ways to find botnets. Study on the vulnerability of deep learning models to adversarial attacks can enhance the robustness of DGA detection mechanism. This paper proposes CLETer, an improved DGA that provides a character-level evasion technique against state-of-the-art DGA classifiers. Based on existing DGA domain names, CLETer can intelligently generate adversarial examples by quantifying the influence of every character to the classification result and then changing the important characters. Those improved domain names can easily evade being detected and show good transferability. The experimental results demonstrate that when modifying only two characters, CLETer can effectively lower the LSTM classifier’s recall from 99.76% to 1.29% and drop the CNN classifier’s recall from 99.36% to 3.64%. It is proved that adversarial retraining is a viable defense strategy to CLETer.