Topological botnet detection datasets and automatic detection with graph neural networks.
A collection of different botnet topologyies overlaid onto normal background network traffic, containing featureless graphs of relatively large scale for inductive learning.
From source
git clone https://github.com/harvardnlp/botnet-detection
cd botnet-detection
python setup.py install
We provide standard and easy-to-use dataset and data loaders, which automatically handle the dataset dnowloading as well as standard data splitting, and can be compatible with most of the graph learning libraries by specifying the graph_format argument:
from botdet.data.dataset_botnet import BotnetDataset
from botdet.data.dataloader import GraphDataLoader
botnet_dataset_train = BotnetDataset(name='chord', split='train', graph_format='pyg')
botnet_dataset_val = BotnetDataset(name='chord', split='val', graph_format='pyg')
botnet_dataset_test = BotnetDataset(name='chord', split='test', graph_format='pyg')
train_loader = GraphDataLoader(botnet_dataset_train, batch_size=2, shuffle=False, num_workers=0)
val_loader = GraphDataLoader(botnet_dataset_val, batch_size=1, shuffle=False, num_workers=0)
test_loader = GraphDataLoader(botnet_dataset_test, batch_size=1, shuffle=False, num_workers=0)
The choices for dataset name are (indicating different botnet topologies):
'chord'(synthetic, 10k botnet nodes)'debru'(synthetic, 10k botnet nodes)'kadem'(synthetic, 10k botnet nodes)'leet'(synthetic, 10k botnet nodes)'c2'(real, ~3k botnet nodes)'p2p'(real, ~3k botnet nodes)
The choices for dataset graph_format are (for different graph data format according to different graph libraries):
'pyg'for PyTorch Geometric'dgl'for DGL'nx'for NetworkX'dict'for plain python dictionary
Based on different choices of the above argument, when indexing the botnet dataset object, it will return a corresponding graph data object defined by the specified graph library.
The data loader handles automatic batching and is agnostic to the specific graph learning library.
We prepare a standardized evaluator for easy evaluation and comparison of different models.
First load the dataset class with BotnetDataset and the evaluation function eval_predictor.
Then define a simple wrapper of your model as a predictor function (see examples), which takes in a graph from the dataset and returns the prediction probabilities for the positive class (as well as the loss from the forward pass, optionally).
We mainly use the average F1 score to compare across models. For example, to get evaluations on the chord test set:
from botdet.data.dataset_botnet import BotnetDataset
from botdet.eval.evaluation import eval_predictor
from botdet.eval.evaluation import PygModelPredictor
botnet_dataset_test = BotnetDataset(name='chord', split='test', graph_format='pyg')
predictor = PygModelPredictor(model) # 'model' is some graph learning model
result_dict_avg, loss_avg = eval_predictor(botnet_dataset_test, predictor)
print(f'Testing --- loss: {loss_avg:.5f}')
print(' ' * 10 + ', '.join(['{}: {:.5f}'.format(k, v) for k, v in result_dict_avg.items()]))
test_f1 = result_dict_avg['f1']
We provide a set of graph convolutional neural network (GNN) models here with PyTorch Geometric, along with the corresponding training script (note: the training pipeline was tested with PyTorch 1.2 and torch-scatter 1.3.1). Various basic GNN models can be constructed and tested by specifing configuration arguments:
- number of layers, hidden size
- node updating model each layer (e.g. direct message passing, MLP, gated edges, or graph attention)
- message normalization
- residual hops
- final layer type
- etc. (check the model API and the training script)
As an example, to train a GNN model on the topological botnet datasets, simply run:
bash run_botnet.sh
With the above configuration, we run graph neural network models (with 12 layers, 32 hidden dimension, random walk normalization, and residual connections) on each of the topologies, and results are as below:
| Topology | Chord | de Bruijn | Kademlia | LEET-Chord | C2 | P2P |
| Test F1 (%) | 99.061 | 99.926 | 98.935 | 99.231 | 98.992 | 98.692 |
| Average | 99.140 | |||||
We also provide labels on the edges under the name edge_y, which can be used for the complete botnet community recovery task, or for interpretation matters.
@article{zhou2020auto,
title={Automating Botnet Detection with Graph Neural Networks},
author={Jiawei Zhou*, Zhiying Xu*, Alexander M. Rush, and Minlan Yu},
journal={AutoML for Networking and Systems Workshop of MLSys 2020 Conference},
year={2020}
}
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