NetSentryx PRO 🛡️

AI-Driven, High-Performance Real-Time Network Observability & Threat Detection Engine

NetSentryx PRO is a next-generation, event-driven network monitoring and threat intelligence platform. Combining rule-based signatures with a PyTorch Deep Autoencoder, it provides sub-10ms packet and netflow anomaly detection, automated multi-channel alerting (Slack, Discord, Email), and an interactive real-time SOC dashboard.

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🏗️ System Architecture

The following flow diagram illustrates the data ingestion, real-time deep learning inference, alerting pipeline, and WebSocket visualization layer:

flowchart TD
    subgraph Capture & Ingestion
        A[Network Interface / Packet Simulator] -->|Raw Traffic Events| B(Lightweight Event Bus)
        B -->|Packet Stream| C[Flow Aggregation Engine]
    end

    subgraph Deep Learning Inference
        C -->|Aggregated Telemetry Vectors| D[PyTorch TrafficAutoencoder]
        D -->|MSE Reconstruction Loss| E{Threshold Evaluation}
    end

    subgraph Alert Dispatch
        E -->|No Anomaly| F[Status: Optimal]
        E -->|Anomaly Detected| G[FastAPI BackgroundTasks]
        G -->|Save Event| H[(SQLite Datastore)]
        G -->|Broadcast WebSocket| I[HTML Live Dashboard]
        G -->|Dispatch Webhooks| J[Slack / Discord / Custom Webhook]
        G -->|Dispatch SMTP| K[Email Alerting]
    end

    style D fill:#EE4C2C,stroke:#fff,stroke-width:2px,color:#fff
    style I fill:#005571,stroke:#fff,stroke-width:2px,color:#fff
    style G fill:#00bf8f,stroke:#fff,stroke-width:2px,color:#fff

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🚀 Key Features

• ⚡ Real-Time Traffic Ingestion: Aggregates packet frames into rich netflow summaries on the fly with minimal latency.
• 🧠 PyTorch Deep Autoencoder Anomaly Detection: Utilizes a lightweight PyTorch neural network (TrafficAutoencoder) to detect sophisticated zero-day exploits, scanning, or DDoS attacks based on multivariate feature reconstruction error.
• 📈 Zero-Manual-Training Startup: Features automated synthetic fitting and statistical threshold calibration (Mean + 3 * Std) during server startup, making it fully functional out of the box.
• ⏱️ Sub-10ms Inference Latency: Optimized inference pipeline ensures real-time evaluation of high-throughput packet flows.
• 📣 Multi-Channel Alert Dispatching: Asynchronously pushes detailed intrusion alerts to Slack webhooks, Discord webhooks, Custom HTTP APIs, and SMTP emails using FastAPI's BackgroundTasks.
• 📊 Interactive SOC Dashboard: Elegant, dark-mode dashboard powered by Tailwind CSS, Chart.js, and WebSockets for real-time visualization of throughput metrics and live threat feeds.

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📁 Project Structure

NetSentryx/
├── config/
│   ├── config.yaml          # System, dashboard, and alerting channel configurations
│   └── rules.yaml           # Threat detection thresholds and rule properties
├── src/
│   ├── alerts/
│   │   ├── ml_detection.py  # PyTorch TrafficAutoencoder & feature scaling
│   │   ├── manager.py       # Async alert dispatcher (Discord, Slack, Email)
│   │   └── detection.py     # Rule-based packet signature detection engine
│   ├── core/
│   │   ├── config_loader.py # Configuration loader utilities
│   │   ├── logger.py        # Database-integrated system logger
│   │   └── time_utils.py    # Timezone-aware timestamp utilities
│   ├── dashboard/
│   │   ├── templates/
│   │   │   └── index.html   # Live HTML5 Dashboard template
│   │   └── server.py        # FastAPI API Router and WebSocket broadcast controller
│   ├── database/
│   │   ├── manager.py       # SQLite connection & database actions
│   │   └── models.py        # SQLAlchemy schema definitions (Alerts, Logs, Flows)
│   ├── flows/
│   │   ├── capture.py       # Scapy packet sniffer thread
│   │   └── processor.py     # Flow aggregation and metric extractor
│   └── main.py              # Application entrypoint
└── main.py                  # Root script runner

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🛠️ Quickstart & Setup Guide

1. Prerequisites

Ensure you have Python 3.10+ installed. If capturing live network interfaces:

• Windows: Install Npcap (select "Install Npcap in WinPcap API-compatible Mode").
• Linux: Install libpcap-dev via sudo apt-get install libpcap-dev.

2. Installation

Clone the repository and install dependencies (using uv is highly recommended for faster installation):

# Clone the repository
git clone https://github.com/yourusername/netsentryx.git
cd netsentryx

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install requirements
pip install -r requirements.txt

3. Environment Setup (.env)

Create a .env file in the project root to configure notification credentials:

# Discord Configuration
DISCORD_ENABLED=true
DISCORD_WEBHOOK_URL="https://discord.com/api/webhooks/your-webhook-id/your-webhook-token"

# Slack Configuration
SLACK_ENABLED=true
SLACK_WEBHOOK_URL="https://hooks.slack.com/services/YOUR_WORKSPACE_ID/YOUR_CHANNEL_ID/YOUR_SECRET_TOKEN"

# SMTP Email Configuration
SMTP_ENABLED=true
SMTP_SERVER="smtp.gmail.com"
SMTP_PORT=587
SMTP_USERNAME="your-soc-email@gmail.com"
SMTP_PASSWORD="your-app-password"
SMTP_FROM_EMAIL="your-soc-email@gmail.com"
SMTP_TO_EMAIL="admin@domain.com"

4. Running the Platform

🧪 Simulation Mode (Includes synthetic traffic simulator)

python main.py --simulate

🌐 Live Sniffing Mode (Requires administrator/sudo privileges)

python main.py

Open your browser and navigate to http://localhost:8000 to view the live dashboard.

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📖 API Documentation

Telemetry & Inference Endpoint

• URL: /api/v1/telemetry/analyze
• Method: POST
• Content-Type: application/json

Request Payload

{
  "packet_count": 550,
  "byte_count": 850000,
  "flow_duration": 4.5,
  "syn_flag_ratio": 0.85,
  "port_entropy": 3.2,
  "byte_rate": 188888.8,
  "source_ip": "192.168.1.120",
  "dest_ip": "10.0.0.5",
  "protocol": "TCP"
}

Response (Normal Traffic)

{
  "status": "NORMAL",
  "threat_level": "low",
  "anomaly_score": 0.3083,
  "threshold": 2.1319
}

Response (Anomalous Traffic)

{
  "status": "ANOMALOUS",
  "threat_level": "critical",
  "anomaly_score": 117999.03,
  "threshold": 2.1319,
  "alert": {
    "id": 12,
    "timestamp": "2026-08-05T14:13:31.373Z",
    "rule_id": "ml_autoencoder_anomaly",
    "rule_name": "ML Autoencoder Anomaly",
    "source_ip": "192.168.1.120",
    "severity": "critical",
    "description": "Deep autoencoder reconstruction error exceeded anomaly threshold (score: 117999.0300, threshold: 2.1320).",
    "metrics": { ... }
  }
}

Historical Queries

Endpoint	Method	Description	Sample Response
/api/alerts	GET	Fetches recent threat alerts saved in DB	[{"id": 1, "rule_name": "Port Scanning", ...}]
/api/flows	GET	Fetches recent network flow telemetry logs	[{"source_ip": "192.168.1.10", "packet_count": 45, ...}]
/api/logs	GET	Fetches backend system audit logs	[{"level": "INFO", "message": "ML engine trained...", ...}]

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🛡️ License

This project is licensed under the MIT License - see the LICENSE file for details.