🛡️ CyberScope-AI: IoT Security Information & Event Management (SIEM)

CyberScope-AI is a lightweight, machine-learning-powered SIEM platform designed specifically for monitoring IoT edge networks. It ingests real-time telemetry from edge nodes (like ESP32 sensors), detects hardware anomalies or security threats using unsupervised machine learning, and visualizes the data in a secure, enterprise-grade Security Operations Center (SOC) dashboard.

📸 Interface Showcase

1. SOC Dashboard & Real-Time Analytics

2. Incident Management & AI Threat Classification

3. ML Benchmarking & Security Analytics

4. Deep Log Analyzer

📊 Project Research Poster

Here is the official academic research poster summarizing the architecture, methodology, system flowchart, and evaluation of CyberScope-AI:

✨ Key Features

• Unsupervised Machine Learning: Utilizes scikit-learn (Isolation Forest) to establish operational baselines and detect zero-day thermal/power anomalies without relying on static thresholds.
• Secure SOC Dashboard: A responsive, dark-mode web interface built with Flask and styled with modern glassmorphism CSS. Protected by Flask-Login session management and password hashing.
• Real-Time Analytics: Integrates Chart.js for dynamic visualization of traffic distributions and temperature volatility trackers.
• Automated PDF Compliance Reporting: Generates downloadable, audit-ready PDF incident reports via the ReportLab engine.
• IoT Edge Simulator: Includes a Python-based ESP32 mock simulator (mock_esp32.py) to stream randomized telemetry and inject realistic hardware threats into the API.
• Automated Testing: Fully verified backend logic and ML models using a comprehensive pytest testing suite.

🛠️ Technology Stack

• Backend: Python 3, Flask, SQLite3, Werkzeug (Security)
• Machine Learning: scikit-learn, NumPy, Joblib
• Frontend: HTML5, CSS3, JavaScript, Chart.js
• Testing & Tooling: Pytest, ReportLab (PDFs), Batch Scripting

📂 Project Structure

├── app.py                  # Main Flask application and API routing
├── start_cyberscope.bat    # Windows launcher for automated startup
├── requirements.txt        # Python dependency list
├── database/
│   └── db_setup.py         # SQLite schema initialization
├── models/
│   └── anomaly_model.pkl   # Pre-trained ML baseline model
├── sim/
│   └── mock_esp32.py       # IoT edge sensor simulator
├── tests/
│   └── test_cyberscope.py  # Pytest automated test suite
├── utils/
│   ├── detector.py         # ML heuristics and classification logic
│   ├── parser.py           # IoT payload sanitization
│   └── pdf_generator.py    # ReportLab PDF creation logic
├── static/css/             # Stylesheets (cyberscope.css)
└── templates/              # HTML Views (dashboard, incidents, analyzer)