Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

87 lines
2.9 KiB
Markdown

---
name: implementing-network-deception-with-honeypots
description: Deploy and manage network honeypots using OpenCanary, T-Pot, or Cowrie to detect unauthorized access, lateral
movement, and attacker reconnaissance.
domain: cybersecurity
subdomain: deception-technology
tags:
- deception
- honeypot
- opencanary
- cowrie
- t-pot
- detection
- lateral-movement
- network-security
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- DE.CM-01
- DE.AE-06
- PR.IR-01
---
# Implementing Network Deception with Honeypots
## When to Use
- When deploying deception technology to detect lateral movement
- To create early warning indicators for network intrusion
- During security architecture design to add detection depth
- When monitoring for unauthorized internal scanning or credential theft
- To gather threat intelligence on attacker techniques and tools
## Prerequisites
- Linux server or VM for honeypot deployment (Ubuntu 22.04+ recommended)
- Python 3.8+ with pip for OpenCanary installation
- Docker for T-Pot or containerized deployment
- Network segment with appropriate VLAN configuration
- SIEM integration for alert forwarding (syslog, webhook, or file-based)
- Firewall rules allowing inbound connections to honeypot services
## Workflow
1. **Plan Deployment**: Select honeypot types and network placement strategy.
2. **Install Honeypot**: Deploy OpenCanary, Cowrie, or T-Pot on dedicated host.
3. **Configure Services**: Enable emulated services (SSH, HTTP, SMB, FTP, RDP).
4. **Set Up Alerting**: Configure log forwarding to SIEM and alert channels.
5. **Deploy Canary Tokens**: Place credential files, shares, and DNS entries.
6. **Monitor Interactions**: Analyze honeypot logs for attacker activity.
7. **Tune and Maintain**: Update configurations based on detection results.
## Key Concepts
| Concept | Description |
|---------|-------------|
| OpenCanary | Lightweight Python honeypot with modular service emulation |
| Cowrie | Medium-interaction SSH/Telnet honeypot capturing commands |
| T-Pot | Multi-honeypot platform with ELK stack visualization |
| Canary Token | Tripwire credential or file that alerts when accessed |
| Low-Interaction | Emulates services at protocol level without full OS |
| High-Interaction | Full OS honeypot capturing complete attacker sessions |
## Tools & Systems
| Tool | Purpose |
|------|---------|
| OpenCanary | Modular honeypot daemon with service emulation |
| Cowrie | SSH/Telnet honeypot with session recording |
| T-Pot | All-in-one multi-honeypot platform |
| Dionaea | Malware-capturing honeypot for exploit detection |
| Splunk/Elastic | SIEM for honeypot alert aggregation |
## Output Format
```
Alert: HONEYPOT-[SERVICE]-[DATE]-[SEQ]
Honeypot: [Hostname/IP]
Service: [SSH/HTTP/SMB/FTP/RDP]
Source IP: [Attacker IP]
Interaction: [Login attempt/Port scan/File access]
Credentials Used: [Username:Password if applicable]
Commands Executed: [For SSH honeypots]
Risk Level: [Critical/High/Medium/Low]
```