EvolveGCNO_improved

This is the implementation of EvolveGCNO-improved model defined in Misbehavior Detection with Spatio-Temporal Graph Neural Networks paper.

Please kindly cite the paper as

@article{yuce2024misbehavior,
  title={Misbehavior detection with spatio-temporal graph neural networks},
  author={Yuce, Mehmet Fatih and Erturk, Mehmet Ali and Aydin, Muhammed Ali},
  journal={Computers and Electrical Engineering},
  volume={116},
  pages={109198},
  year={2024},
  publisher={Elsevier}
}

Steps

The model generation consists of three stages.

• Adding clustering information to BURST-Adma dataset's each timestep.
• Generating Graph Neural Networks (GNNs) dataset
• Hyperparameters search
• Model tries

Setup

Get cuda version

nvcc --version

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Jun__8_16:49:14_PDT_2022
Cuda compilation tools, release 11.7, V11.7.99
Build cuda_11.7.r11.7/compiler.31442593_0

Installing conda

From: https://docs.anaconda.com/miniconda/

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh

Do not forget to update .bashrc

From Yaml

conda env create --name egcnoi_env --file=export.yaml
conda activate egcnoi_env

From Command Line (Not properly tested!)

conda create --name egcnoi python=3.10.9 ipython
conda activate egcnoi

# conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

# conda install pyg -c pyg
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.1+cu117.html

pip install torch-geometric-temporal

conda install -c anaconda pandas
conda install -c anaconda numpy

conda install -c conda-forge matplotlib
conda install -c anaconda seaborn
conda install -c conda-forge scikit-learn
conda install -c conda-forge tqdm
conda install -c anaconda ipywidgets

pip install hiddenlayer
pip install torchviz
conda install -c conda-forge ipympl

Adding Clustering Information to BURST-Adma Dataset's each Timestep.

add_clustering_information.ipynb

Generating Graph Neural Networks (GNNs) dataset

generate_gnn_datasets.ipynb

Hyperparameters search

hyper_parameter_search.ipynb

Model Tries

model_tries.py and model_tries.ipynb contains manual tests that can be conducted for experiemntations.