BotDetector: a system for identifying DGA-based botnet with CNN-LSTM

Botnets are one of the major threats to network security nowadays. To carry out malicious actions remotely, they heavily rely on Command and Control channels. DGA-based botnets use a domain generation algorithm to generate a significant number of domain names. By analyzing the linguistic distinctions between legitimate and DGA-based domain names, traditional machine learning schemes obtain great benefits. However, it is difficult to identify the ones based on wordlists or pseudo-random generated. Accordingly, this paper proposes an efficient CNN-LSTM-based detection model (BotDetector) that uses only a set of simple-to-compute, easy-to-compute character features. We evaluate our model with two open-source benchmark datasets (360 netlab, Bambenek) and real DNS traffic from the China Education and Research Network. Experimental results demonstrate that our algorithm improves by 1.6 $$\%$$ % in terms of accuracy and F1-score and reduces the computation time by 9.4 $$\%$$ % compared to other state-of-the-art alternatives. Remarkably, our work can identify botnet’s covert communication channels that use domain names based on word lists or pseudo-random generation without any help of reverse engineering.