Decepti-Node

Objective

Build a repeatable way to deploy and recycle vulnerable honeypot environments in a home lab without manual VM/LXC setup each time.

Tech Stack

• Proxmox VE
• Linux (LXC and VM targets)
• Docker
• Python

Tooling Used

• Core engine: Proxmox VE for hypervisor-level orchestration and fast target lifecycle control.
• Automation layer: Python selected for rapid provisioning scripts and repeatable infra tasks.
• Ops workflow: Linux-native tooling and container primitives (LXC/Docker) to keep deployments lightweight and reproducible.
• Why this stack: Maximizes practical systems-administration signal and resource-efficiency under homelab constraints.

Architecture

Decepti-Node uses Proxmox as the orchestration base and Python automation to provision isolated targets from predefined templates.
Each target is configured with constrained compute resources, predictable network placement, and service-level configuration for honeypot scenarios.
The lifecycle flow is: provision -> configure -> monitor -> archive telemetry -> destroy/rebuild.

Execution

I started by standardizing target images and defining a resource profile for each scenario so deployment would remain predictable even under concurrent runs.
That reduced manual drift and made troubleshooting reproducible across rebuilds.

Then I automated provisioning and post-deploy configuration with Python, including service bootstrap and isolation checks.
I tuned CPU/RAM allocations against host capacity so multiple decoy services could run in parallel without starving the hypervisor.

Results

Decepti-Node turned ad hoc honeypot setup into a repeatable defensive workflow.
I improved consistency, reduced deployment time, and gained stronger practical experience in Proxmox lifecycle management under resource constraints.

Next improvements: add richer telemetry exports and automated scenario profiles based on attack class.