| license: apache-2.0 | |
| task_categories: | |
| - tabular-classification | |
| tags: | |
| - graph-neural-network | |
| - cybersecurity | |
| - node-classification | |
| - pytorch | |
| # MeshGraph GCN | |
| MeshGraph is a graph convolutional node classifier for detecting compromised assets | |
| inside a simulated enterprise network. Nodes carry host telemetry, while edges encode | |
| observed communication. Compromise begins at sparse seeds and propagates stochastically | |
| through the network. | |
| The GCN is compared with a logistic-regression baseline that sees identical host | |
| features but cannot use graph structure. | |
| This is a transductive benchmark: the graph and all node features are visible during | |
| training, while validation and test labels remain hidden. | |
| ## Reproduce | |
| ```powershell | |
| uv run python projects/meshgraph-gcn/generate_data.py | |
| uv run python projects/meshgraph-gcn/train.py | |
| ``` | |
| ## Verified results | |
| The generated graph contains 600 nodes, 1,689 undirected communication edges, six | |
| subnets, and a 23.33% compromise rate. The final test contains 120 nodes: | |
| | Model | Parameters | Accuracy | ROC-AUC | Average precision | F1 | | |
| | --- | ---: | ---: | ---: | ---: | ---: | | |
| | Feature-only logistic regression | 9 fitted coefficients | 78.33% | 0.8362 | 0.6274 | 0.5938 | | |
| | Two-layer GCN | 802 | **81.67%** | **0.8564** | **0.6976** | **0.6333** | | |
| Both thresholds were selected independently on the same 120-node validation split. | |
| The GCN improved F1 by 3.96 points and average precision by 7.02 points. | |