## 4 Experiments
### Experimental Settings  
In this paper, three existing methods were selected for comparison: the [PERSON] method [20] based on meaningful word segmentation, the ATT-CNN-BiLSTM method [34] based on local features extracted by CNN and LSTM.MI method [5] based on character LSTM. (1) [PERSON] et al. (2018) [20] used Wordinja [46] for word segmentation and extracted context-sensitive word embedding features through ELMo [39]. The mean values of each dimension of word features were calculated for classification. This method is suitable for the detection of dictionary-based AGDs. (2) ATT-CNN-BiLSTM (2020) [34] used a CNN to extract local features and used bidirectional LSTM to extract sequence features for classification. This method is suitable for the detection of both dictionary-based and character-based AGDs. (3) LSTM.MI (2018) [5] used the LSTM model for unbalanced AGD classification. Since the datasets we constructed were roughly balanced, this method is basically the same as the method of [PERSON] (2016) [4], and this method is suitable for the detection of character-based AGDs. Our experiments could be divided into three categories: the comparison experiments with existing detection methods, the ablation experiments, and the comparison experiment with detection engines. **(1) Comparison experiments with existing detection methods**  
We conducted 4 groups of compassion experiments, to compare the overall detection effects, the recall rate of certain DGA families, the detection ability on real-world samples, and the detection ability against adversarial DGA families between our method and the three comparison methods. The details of the experiments are as follows. * **Comparison experiment for detection effects. ** We compared our method with the Koh method, ATT-CNN-BiLSTM method, and LSTM. MI method on the three datasets. * **Comparison experiment for family recall. ** We compared the recall rate of each DGA family of our method with the Koh method, ATT-CNN-BiLSTM method, and LSTM.MI method. For each method, we used the final model trained on the full dataset and calculated the recall rate of each DGA family in the full dataset to show the detection ability of different methods for each DGA family. * **Comparison experiment for generalization recall. ** We compared the recall rate of each generalization dataset of our method with the Koh method, ATT-CNN-BiLSTM method, and LSTM.MI method. For each method, we used the final model trained on the full dataset to test the recall rate on the three generalization datasets constructed based on DGArchive and Netlab 360 to show the detection ability on real-world samples. * **Comparison experiment for adversarial DGA families. ** Based on adversarial DGA families named _deception_ and _deception2_ proposed by [PERSON] et al. [42], we reconstructed a new dataset to compare the detection effects on such kinds of DGA families with the Koh method, ATT-CNN-BiLSTM method, and LSTM.MI method. **(2) Ablation experiments**  
Our method fuses a variety of features. We conducted 3 groups of ablation experiments for the selection of word distribution features, the selection of n-gram sequence features, and feature fusion. The details of the experiments are as follows. * **Effectiveness experiment for word distribution features. ** We compared the detection effects of two-word segmentation methods, including Wordinja [46] and WordSegment [47], and the detection effects of two kinds of features, including Mean and Std, to select the best word distribution features. * **Effectiveness experiment for n-gram sequence. ** We compared the detection effects of 1-gram, 2-gram and 3-gram sequence features and the detection effects of different combinations of n-gram sequence features to select the best combination of n-grams. * **Effectiveness experiment for feature fusion. ** We added each type of feature in sequence and observed the changes in the detection effect to verify the effectiveness of each type of feature.