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adarsh-aur/
stage6-rgcn-security

Name: stage6-rgcn-security
Rating: 2.5 (5 reviews)

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🛡️ Stage 6 — Heterogeneous Structural GNN (RGCN)

🔍 Multi-Cloud Threat Detection Pipeline — Holistic Version

A schema-agnostic, relation-aware Graph Neural Network for detecting compromised entities across multi-cloud environments using Relational Graph Convolutional Networks (RGCN).

⚙️ Core Design

Library: PyTorch Geometric (RGCNConv)
Graph Type: Heterogeneous (multi-node, multi-relation)
Design Goal: Fully schema-agnostic
Feature Handling: Supports any input dimension

🔗 Additional Components

PEFT Adapters → Efficient fine-tuning
DistMult Edge Scoring → Relation-level anomaly detection
Threat Heads → Node-level classification

🔬 Ablation Study

Controlled experiment: identical data, seed, and hyperparameters
Only the GNN operator changes

Model	Type	Params	User AUC	User AP	Role AUC	Role AP
RGCN	⭐ PRIMARY	8,515,599	0.5	0.5	0.5	0.5
GCN	Baseline	5,219,855	0.5	0.5	0.5	0.5
GAT	Baseline	5,320,207	0.5	0.5	0.5	0.5
SAGE	Baseline	5,383,695	0.5	0.5	0.5	0.5

⚠️ Note on Results

These metrics reflect Phase 1 conditions:

Small graph (~tens of nodes)
Limited feature signal (near-zero embeddings)
Class imbalance effects

➡️ As a result:

All models converge to random baseline (AUC ≈ 0.5)
This is expected behavior, not a failure

Performance becomes meaningful in Phase 2 with real embeddings.

📦 Model Characteristics

Property	Value
MAX_FDIM	1024
Hidden Dim	256
Output Dim	128
Relations	20
Layers	3
Adapter Rank	16

🚀 Usage (Stage 7 Integration)

python

import torch
from huggingface_hub import hf_hub_download

ckpt = torch.load(
    hf_hub_download(
        repo_id="adarsh-aur/stage6-rgcn-security",
        filename="model_RGCN.pt"
    )
)

model = HeteroRGCN()
model.load_state_dict(ckpt['model_state_dict'])
model.eval()

with torch.no_grad():
    h_v, offsets, logits = model(graph_snapshot)

# h_v: [total_nodes, 128]
# → Feed into Stage 7 (GRU / temporal model)

🧩 Key Capabilities

✔ Handles any node/edge schema
✔ Supports future unseen cloud resources
✔ Learns relation-specific transformations
✔ Enables edge-level anomaly detection
✔ Ready for temporal extension (Stage 7)

📉 Current Limitations

Phase 1 uses minimal feature signal
Small dataset limits generalization
Metrics do not yet reflect full capability

🔮 Next Stage

Stage 7 — Temporal Graph Modeling

Sequence modeling over time (GRU / Transformer)
Detect evolving attack patterns
Improve recall on multi-step attacks

👤 Author

Adarsh
Multi-Cloud Security ML Research

⭐ Summary

This stage establishes the structural backbone of the pipeline:
A flexible, relation-aware GNN that can scale to real-world cloud security graphs.