Researchers propose Chameleon, an adaptive AI-driven honeypot architecture for cyber deception

A new arXiv preprint introduces Chameleon, a honeypot framework that adjusts its behavior on the fly using swarm-optimization tuned to threat levels and semantic deception planning built on rapidly-exploring random trees. The approach targets a key weakness of decoy systems, namely that experienced intruders can unmask them after a handful of routine probes, while also serving as a lower-cost alternative to enterprise deception suites priced in the six figures. The version-2 paper appears in the cs.AI category.