Traditional　techniques　for　detecting　threats　in　mobile　edge　networks　are　limited　in　their　ability　to　adapt　to　evolving　threats.　We　propose　an　intelligent　reinforcement　learning　(RL)-based　method　for　real-time　threat　detection　in　mobile　edge　networks.　Our　approach　enables　an　agent　to　continuously　learn　and　adapt　its　threat　detection　capabilities　based　on　feedback　from　the　environment.　Through　experiments,　we　demonstrate　that　our　technique　outperforms　traditional　methods　in　detecting　threats　in　dynamic　edge　network　environments.　The　intelligent　and　adaptive　nature　of　our　RL-based　approach　makes　it　well　suited　for　securing　mission-critical　edge　applications　with　stringent　latency　and　reliability　requirements.　We　provide　an　analysis　of　threat　models　in　multiaccess　edge　computing　and　highlight　the　role　of　on-device　learning　in　enabling　distributed　threat　intelligence　across　heterogeneous　edge　nodes.　Our　technique　has　the　potential,　significantly　enhancing　threat　visibility　and　resiliency　in　next-generation　mobile　edge　networks.　Future　work　includes　optimizing　sample　efficiency　of　our　approach　and　integrating　explainable　threat　detection　models　for　trustworthy　human-AI　collaboration.　The　RL-based　method　improves　threat　detection　in　cloud　networks,　offering　an　adaptive　and　intelligent　approach　suitable　for　securing　mission-critical　applications.　Future　work　includes　optimizing　sample　efficiency　and　integrating　explainable　threat　detection　models.image