Merge pull request #7 from tracebit-com/codex/add-article-link-to-documentation

Q-Cowrie: Reinforcement Learning for Adaptive Honeypot Deception

Author: andy-smith-tracebit

README.MD

@@ -54,6 +54,7 @@ An awesome collection of articles, papers, conferences, guides, and tools relati
 - [SoK: Honeypots & LLMs, More Than the Sum of Their Parts?](https://arxiv.org/abs/2510.25939) (2025) - systematizes LLM-powered honeypot research and evaluation trends.
 - [HoneyTrap: Deceiving Large Language Model Attackers to Honeypot Traps with Resilient Multi-Agent Defense](https://arxiv.org/abs/2601.04034) (2026) - proposes a deceptive LLM defense framework with multi-agent coordination, plus a progressive jailbreak dataset and new metrics for measuring misdirection and attacker cost. 
 - [Measuring the Efficacy of Cyber Deception](https://www.techrxiv.org/doi/full/10.36227/techrxiv.176834017.70221537) (2026) - examines how to measure cyber deception effectiveness by reviewing existing evaluation approaches and proposing new metrics and frameworks to assess deceptive tactics in modern, AI-augmented threat environments.
+- [Q-Cowrie: Reinforcement Learning for Adaptive Honeypot Deception](https://link.springer.com/article/10.1007/s10207-026-01221-5) (2026) - presents “Q-Cowrie,” a reinforcement learning-enhanced Cowrie honeypot that models attacker decisions with an MDP and adapts responses during attacker interaction.
 
 ### Code Repositories
 - [Evaluating Deception and Moving Target Defense with Network Attack Simulation](https://github.com/dfki-in-sec/NASIM-MTD)