- data set domain-generation-algorithm from kaggle, contains 1 million DGA domain and top 1 million good domain
https://www.kaggle.com/code/slashtea/domain-name-classifier/input?select=dga_project_dga_domain_list_clean.txt
Model uses an embedding layer, followed by a 1D convolutional layer, a max-pooling layer, an LSTM layer, and a final dense layer for binary classification. This architecture is in line with the recommendation for DGA detection, combining the strengths of both CNNs and LSTMs.
Embedding Layer: Convert integer-encoded domain names into dense vectors. 1D Convolutional Layer: Detect local patterns. Max Pooling Layer: Reduce dimensionality. RNN/LSTM/GRU Layer: Capture patterns across sequence length. Dense Layer: Fully connected layer for classification. Output Layer: Binary classification (DGA or benign).
CNNs are traditionally used for image processing where spatial hierarchies are present. However, 1D CNNs can be used for sequences and have been found effective in detecting local patterns in a sequence. Domain names generated by DGAs often have patterns that can be recognized by 1D CNNs. They can learn and detect local patterns in sequences, which is helpful for DGA detection.
RNNs, especially their gated variants like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), are great for sequence data since they have memory of past data points. They can capture patterns over time (or sequence length) which can be useful if there's some pattern in the order of characters in DGA domains versus benign domains.
You can also combine 1D CNNs and RNNs. Start with 1D CNN layers to detect local patterns and then feed the output to RNN layers to capture global patterns across the sequence.
It's common to start the neural network with an embedding layer when working with categorical data like sequences. This layer can transform integer-encoded domain names into dense vectors of fixed size which can be more informative.
For sequence data like domain names, consider techniques such as: Introducing slight character permutations. Flipping the order of some characters. This can help the model become more robust and generalize better.
GRUs: Instead of LSTMs, try using Gated Recurrent Units (GRUs), which can sometimes perform comparably with fewer parameters. Bidirectional LSTMs or GRUs: These can capture patterns from both the start and end of sequences. Transformer Architectures: Consider using transformer-based models, which have shown state-of-the-art results in various sequence tasks.
Extract additional features from the domains, such as the length of the domain, number of vowels, etc., and feed these into the neural network. Use character bigrams or trigrams instead of single characters.
Learning Rate: Adjust the learning rate of the Adam optimizer. Sometimes a smaller learning rate can lead to more stable training, but it might require more epochs. Batch Size: Experiment with different batch sizes. A smaller batch size can lead to more stable training, while a larger one can speed up the training process. Epochs: Monitor the validation loss and accuracy. If the model hasn't started overfitting, consider training for more epochs.
More Layers: Add more convolutional or LSTM layers to capture more complex patterns. Filter Sizes: Experiment with different filter sizes in the convolutional layers to capture patterns of varying lengths. Number of Filters: Increase the number of filters in the convolutional layers. Dropout: Adjust dropout rates or add dropout layers in different parts of the network to prevent overfitting. Regularization: Add L1 or L2 regularization to the dense layers.