Adaptive Honeypots: A Defender That Decides, Not Just Detects

A complete, runnable Q-learning agent deciding how deeply to engage an attacker session, a real reward-curve chart showing the agent learning over 300 episodes, and why static honeypots like Cowrie eventually get fingerprinted.

~5 min total·4 quadrants of structured content

By the end of this topic, you will

Trace a minimal Q-learning honeypot agent's state, actions and reward function in code
Read a reward-per-episode training curve and explain what a rising moving average shows
Explain why a static, scripted honeypot is eventually fingerprinted and how an adaptive one addresses that weakness

Quadrant 1 · e-Tutorial

Video lectures and walkthroughs

Video content coming soon.

Quadrant 2 · e-Content

Articles and case studies

Adaptive Honeypots: A Defender That Decides, Not Just Detects

A traditional honeypot answers every connection the same scripted way. An adaptive honeypot treats each session as a small reinforcement learning episode, deciding, session by session, how deeply to engage.

Quadrant 3 · Web Resources