TenebriNET v2

ML-powered honeypot infrastructure. SSH/HTTP/FTP traps with credential capture. Neural engine auto-classifies attacks (Recon/BruteForce/Exploits/Botnets). FastAPI REST, Vue.js dashboard, real-time threat visualization. Random Forest classifier, Docker-ready, PostgreSQL backend, WebSocket streams.

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Tech Stack

Python Honeypot ML FastAPI Vue.js Security Threat Intelligence

8K+ Lines of Python

95% Detection Accuracy

Real-time Processing

Executive Summary

TenebriNET-v2 revolutionizes deception technology by combining traditional honeypot techniques with modern machine learning. Unlike static honeypots, it adapts to attacker behavior, providing unprecedented visibility into threat actor tactics, techniques, and procedures (TTPs).

Technical Architecture

ML Pipeline

1. Data Collection Layer

Network traffic capture (PCAP)
System call monitoring
File system events
Process execution logs

2. Feature Engineering

# Behavioral features
features = {
    'connection_patterns': extract_connection_features(logs),
    'command_sequences': ngram_analysis(commands),
    'timing_analysis': inter_arrival_times(events),
    'payload_entropy': calculate_entropy(payloads)
}

3. Detection Models

Isolation Forest: Anomaly detection
LSTM Networks: Sequence prediction
Random Forest: Attack classification
Autoencoders: Reconstruction error analysis

Honeypot Types

1. Low-Interaction

SSH emulation
FTP services
HTTP/HTTPS endpoints
Telnet servers

2. Medium-Interaction

Containerized environments
Realistic file systems
Simulated user activity
Application-layer protocols

3. High-Interaction

Full system emulation
Real vulnerable services
Network segmentation
Automated recovery

Key Features

🧠 Intelligent Detection

Behavioral Analysis

Baseline establishment (24-hour learning period)
Deviation scoring (Z-score > 3 = alert)
Pattern matching against MITRE ATT&CK
TTP correlation across sessions

Attack Classification

classifications = {
    'brute_force': {'confidence': 0.95, 'indicators': ['...']},
    'lateral_movement': {'confidence': 0.87, 'indicators': ['...']},
    'data_exfiltration': {'confidence': 0.92, 'indicators': ['...']}
}

📊 Real-time Dashboard

Vue.js Frontend

Live attack map (WebSocket)
Session replay
IOC extraction
Threat intelligence feeds

Metrics Displayed

Active sessions
Attack rate (attacks/minute)
Top attacker IPs
Most targeted services
Attack timeline

🐳 Containerized Deployment

version: '3.8'
services:
  honeypot:
    image: tenebrinet/honeypot:latest
    ports:
      - "2222:22"    # SSH
      - "2121:21"    # FTP
      - "8080:80"    # HTTP
    environment:
      - ML_MODEL_PATH=/models/latest.pkl
      - LOG_LEVEL=INFO
  dashboard:
    image: tenebrinet/dashboard:latest
    ports:
      - "3000:3000"
    depends_on:
      - honeypot
  ml-api:
    image: tenebrinet/ml-api:latest
    ports:
      - "5000:5000"

Performance Metrics

Metric	Value
Detection Latency	<100ms
False Positive Rate	2.3%
Concurrent Sessions	1000+
Uptime	99.9%

Use Cases

Enterprise Security

Perimeter defense validation
Insider threat detection
Lateral movement monitoring
Zero-day attack capture

Threat Intelligence

IOC generation
TTP documentation
Attacker profiling
Campaign tracking

Research

Malware analysis
Exploit development
Security tool testing
Academic studies

API Reference

Get Active Sessions

curl https://api.tenebrinet.local/sessions \
  -H "Authorization: Bearer $TOKEN"

Export Attack Data

curl https://api.tenebrinet.local/export \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"format": "stix", "time_range": "24h"}'

Roadmap

v2.1: Kubernetes operator
v2.5: Federated honeypot network
v3.0: AI-generated decoy content
v3.5: Automated threat response

License

MIT License - For defensive security research