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cicids-gat-diagnoser – AI Model by transformer112 | AlphaNeural AI

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transformer112/
cicids-gat-diagnoser

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intrusion-detection

graph-neural-networks

multiclass-classification

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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

python

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)