---
pattern_id: pattern_scada_cybersecurity_framework
applicable_symptoms: [scada_cybersecurity_framework]
domain: Systems_Compute
---

# SCADA systems lack structured cybersecurity defenses against targeted attacks

**Domain**: `Systems_Compute`

## Fix

Implement a multi-layered SCADA cybersecurity framework including network segmentation, intrusion detection, and access control

## Anti-pattern

Relying solely on perimeter firewalls without internal monitoring

## Cross-domain analogies

- **Perception_Vision** → Use learned selective attention to prioritize critical SCADA nodes instead of monitoring all points.
  - related fix: Use deformable cross-attention with learned sampling points to selectively attend to relevant image features instead of the entire grid.
- **Planning_Decision** → Incremental 3D Gaussian map updates inspire sliding-window anomaly detection for real-time SCADA threat localization.
  - related fix: Incremental 3D Gaussian Splatting for online scene reconstruction and localization, using a sliding window of keyframes to update a 3D Gaussian map and match the goal image via rendering-based pose estimation.
- **Learning_Training** → Use the SCADA system itself to filter and score its own traffic for high-confidence anomaly detection.
  - related fix: Self-Refining Data Flywheel (SRDF): after initial training, use the Navigator model itself to filter and score candidate trajectories, retaining only high-confidence or high-reward pairs for iterative fine-tuning.

## Patch

```diff
--- scada_cybersecurity_framework.before.py
+++ scada_cybersecurity_framework.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

+# --- AFTER (ROSCLAW heuristic graft) ---

 # Symptom: SCADA systems lack structured cybersecurity defenses against targeted attacks

+# Fix    : Implement a multi-layered SCADA cybersecurity framework including network segmentation, intrusion detection, and access control

+# Avoid  : Relying solely on perimeter firewalls without internal monitoring

```
