G-Hypeddings
1. Overview
G-hypeddings is a Python library designed for graph hyperbolic embeddings, primarily utilized in detecting cybersecurity anomalies. It includes 06 distinct models with various configurations, all of which utilize hyperbolic geometry for their operations. The library is built on top of the PyTorch framework.
1.1. Models
The models can be divided into three main categories based on the model's overall architecture namely Shallow models (Poincaré), Convolutional-based models (HGCN & HGNN), and Autoencoder-based models (HGCAE & PVAE).
| Name | Year | Encoder | Decoder | Manifold | Ref |
|---|---|---|---|---|---|
| Poincaré | 2017 | / | MLP | Poincaré Ball | [1] |
| HGNN | 2019 | HGCN | MLP | Poincaré Ball, Lorentz | [2] |
| HGCN | 2019 | HGCN | MLP | Lorentz | [3] |
| P-VAE | 2019 | GCN | MLP | Poincaré Ball | [4] |
| H2H-GCN | 2021 | HGCN | MLP | Lorentz | [5] |
| HGCAE | 2021 | HGCN | HGCN | Poincaré Ball | [6] |
In this library, we provide a variety of binary classifiers, clustering algorithms, and unsupervised anomaly detection algorithms to use with the autoencoder-based models (HGCAE & PVAE). All of these are Scikit-learn models tuned using the Grid-Search technique.
| Name | Type |
|---|---|
| Support Vector Machine (SVM) | Binary Classifier |
| Multilayer Perceptrone (MLP) | Binary Classifier |
| Decision Tree | Binary Classifier |
| Random Forest | Binary Classifier |
| AdaBoost | Binary Classifier |
| K-Nearest Neighbors (KNN) | Binary Classifier |
| Naive Bayes | Binary Classifier |
| Agglomerative Hierarchical Clustering (AHC) | Clustering Algorithm |
| DBSCAN | Clustering Algorithm |
| Fuzzy C mean | Clustering Algorithm |
| Gaussian Mixture | Clustering Algorithm |
| K-means | Clustering Algorithm |
| Mean shift | Clustering Algorithm |
| Isolation Forest | Anomaly Detection Algorithm |
| One-class SVM | Anomaly Detection Algorithm |
1.2. Datasets
The following intrusion detection datasets were used to test and evaluate the models. Our code includes all the pre-processing steps required to convert these datasets from tabular format into graphs. Due to usage restrictions, this library provides only a single graph of each dataset, with 5,000 nodes, already pre-processed and normalized.
| Name | Ref |
|---|---|
| CIC-DDoS2019 | [7] |
| AWID3 |
2. Installation
3. Usage
Training and evaluation a model using our library is done in 03 lines of code only!
3.1. Models
3.2. Datasets
4. Citation
5. References
[1]: Nickel, Maximillian, and Douwe Kiela. "Poincaré embeddings for learning hierarchical representations." Advances in neural information processing systems 30 (2017). [2]: Liu, Qi, Maximilian Nickel, and Douwe Kiela. "Hyperbolic graph neural networks." Advances in neural information processing systems 32 (2019). [3]: Chami, Ines, et al. "Hyperbolic graph convolutional neural networks." Advances in neural information processing systems 32 (2019). [4]: Mathieu, Emile, et al. "Continuous hierarchical representations with poincaré variational auto-encoders." Advances in neural information processing systems 32 (2019). [5]: Dai, Jindou, et al. "A hyperbolic-to-hyperbolic graph convolutional network." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. [6]: Park, Jiwoong, et al. "Unsupervised hyperbolic representation learning via message passing auto-encoders." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. [7]: CIC-DDoS2019