Repository

The code available in this repository has been used for producing the results reported in Graph Neural Networks for IoT Security: A Comparative Study

How to use the code

Create Conda Environment

conda create -n anomaly_detection python=3.9
conda activate anomaly_detection
pip install -r requirements.txt
pip install torch==2.1.0+cu118 -f https://download.pytorch.org/whl/torch_stable.html

Dataset Download

mkdir anomaly_detection_dataset

Download dataset snaposhots and stats by following instructions reported here.

Download Dynamic Graphs Dependencies

mkdir gnn-network-analysis/dynamic_graphs
cd gnn-network-analysis/dynamic_graphs
git clone https://github.com/ciccio42/EvolveGCN.git
git checkout pub_iot

Train and Test

cd DOMINANT
# Dominant Train
nohup train.sh > dominant_train.txt &

# Dominant Test
nohup test_tdg.sh > test_tdg.txt & # Test tdg model
nohup test_etdg.sh > test_etdg.txt & # Test e-tdg model

cd OCGNN
# OC-GNN Train
nohup train.sh > dominant_train.txt &

# OC-GNN Test
nohup test_tdg.sh > test_tdg.txt & # Test tdg model
nohup test_etdg.sh > test_etdg.txt & # Test e-tdg model

NOTE Configure the bash file correctly. You need to set the snapshot to use and your paths to dataset.

Note

For any errors and/or questions about the code either open an issue or mail [email protected], with object "QUESTION-CODE: Graph Neural Networks for IoT Security: A Comparative Study"