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cicids-gat-diagnoser
transformer112/cicids-gat-diagnoser
cicids-gat-diagnoser is a machine learning model from transformer112. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
Graph Attention Network for multiclass intrusion detection. Classifies network attacks into 8 unified threat families using graph topology.
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Public
Updated Jun 17, 2026
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At a glance
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Jun 17, 2026
- Updated
- Jun 17, 2026
- SHA
- 4970cc19
Model card
From the Hugging Face model README
CICIDS GAT Diagnoser (Stage 2)
Graph Attention Network for multiclass intrusion detection. Classifies network attacks into 8 unified threat families using graph topology.
Performance
- Macro F1: 90.60%
- Weighted F1: 96.15%
- Classes: 8 Threat Families
Architecture
- Type: Heterogeneous Graph Attention Network
- Node Embeddings: 64-dimensional
- Hidden Layers: 128-dimensional
- Attention Heads: 4
- Edge Features: 58 flow statistics
- Regularization: Dropout(0.3)
Usage
from utils.model_loader import HybridDiagnoserGAT, load_gat_model
model = load_gat_model()
pred_class, confidence, probs = predict_gat(model, features, src_ip, dst_ip)
- Library
- pytorch
- License
- mit
- Created
- Jun 17, 2026
- Updated
- Jun 17, 2026