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-# MalGraph
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+# MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection
+
+
+## 1. Description
+
+In this paper, we propose [**MalGraph**](https://www.github.com), a hierarchical graph  neural network to build an effective and robust Windows PE  malware detection. In particular, MalGraph makes better use  of the hierarchical graph representation which incorporates  the inter-function call graph with intra-function control flow  graphs for representing the executable program.
+If you find this paper or this repo is useful for you, we would like to have you cite our paper as follows.
+
+![system](./MalGraph.png)
+
+Xiang Ling, Lingfei Wu, Wei Deng, Sheng Zhang, Zhenqing Qu, Jiangyu Zhang, Tengfei Ma, Bin Wang, Chunming Wu and Shouling Ji, MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection, IEEE International Conference on Computer Communications (**INFOCOM**) 2022.
+
+ ``` 
+@inproceedings{ling2022malgraph,
+  title={{MalGraph}: Hierarchical Graph Neural Networks for Robust Windows Malware Detection},
+  author={Ling, Xiang and Wu, Lingfei and Deng, Wei and Qu, Zhenqing and Zhang, Jiangyu and Zhang, Sheng and Ma, Tengfei and Wang, Bin and Wu, Chunming and Ji, Shouling},
+  booktitle={IEEE Conference on Computer Communications (INFOCOM)},
+  pages={},
+  year={2022},
+  address={Virtual Event},
+  publisher={IEEE}
+}
+ ```
+
+## 2. Usage
+
+It is necessary to use IDA Pro and the function of `parse_json_list_2_pyg_object()` in `./samples/Preprocess.py` to generate the hierarchical graph representation with `torch_geometric.data.Data` for all file in the training/validation/testing set.
+One illustrating example can be found in the folder of `samples/README.md`
+In addition, we also provide the ``src/utils/Vocabulary.py`` which is the core implementation for us to build and save the vocabulary of external function names for all files in the training set.
+
+After preparing both the preprocess_dataset and train_vocab_file, we can train our model of MalGraph.
+It is noted that all hyper-parameters are configured in ``configs/default.yaml`` which should be first configured manually.
+And then we can directly train our model via ``python DistTrainModel.py``
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