Aegis Architect: AI-Powered Industrial Control & Monitoring

Aegis Architect is a high-performance, real-time industrial monitoring and security system. It features a C++ Core for high-speed telemetry and a Python Intelligence Layer that provides real-time web visualization, SQLite persistence, and predictive anomaly detection with an automated "Kill Switch" mechanism.

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Key Features

• High-Speed Emitter (C++): Simulates industrial sensor data (Voltage & Current) with low-latency UDP broadcasting.
• AI Intelligence (Python): Uses Scikit-Learn trained models to predict anomalies in real-time based on historical power surge patterns.
• Web Dashboard: A responsive, dark-themed Flask-SocketIO interface for live monitoring without page refreshes.
• Automated Defense: Integrated "Kill Switch" that sends a /SHUTDOWN command back to the C++ core upon anomaly detection.
• Reliable Logging: Persistent data storage using SQLite, ensuring all telemetry is recorded for post-incident analysis.
• Bi-Directional Communication: Full-duplex communication between C++ and Python using UDP protocols.

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Tech Stack

Layer	Technologies
Core	C++, WinSock2, UDP Sockets, nlohmann/json
Intelligence	Python 3.12, Scikit-Learn, Joblib, Threading
Web Interface	Flask, Flask-SocketIO, Socket.io (JS)
Database	SQLite3
Theme	Cyberpunk Dark UI

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AI Mechanism

Aegis doesn't just watch; it thinks. The Python "Mind" uses a pre-trained classification model to analyze every incoming packet.

1. Data Ingestion: Receives voltage/current data via UDP.
2. Inference: AI model evaluates the risk level.
3. Action: If an anomaly (surge) is detected, Aegis automatically triggers a remote shutdown of the C++ Core to prevent hardware damage.

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Project Structure

TheArchitect/
├── aegis_app.py        # Main Python Entry (AI, Flask & SocketIO)
├── core/               # C++ Source files (The Emitter & Listener)
├── templates/          # Web Dashboard HTML
├── data/               # SQLite Database (aegis_records.db)
└── models/             # Pre-trained AI Models (aegis_brain.pkl)

##  How to Run
1. Requirements

    MSYS2 (UCRT64) with GCC/G++

    Python 3.12+

    Required Python Packages:
    Bash

    pip install flask flask-socketio scikit-learn joblib

2. Execution

    Start the Intelligence Layer:
    Bash

python aegis_app.py

Ignite the Core: Run your compiled aegis_core.exe from the terminal.

Access the Dashboard: Open your browser and navigate to http://127.0.0.1:5000