A multi-layer cloud IDS uses per-layer ML detection, learned confidence gates, Chroma memory, and LLM escalation with Q-learning adaptive calibration to cut LLM calls by 58.78% while reporting 88.68% overall accuracy.
A compara- tive analysis of self-aware reinforcement learning models for real-time intrusion detection in fog networks
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A Multi-Layer Cloud-IDS Pipeline with LLM and Adaptive Q-Learning Calibration
A multi-layer cloud IDS uses per-layer ML detection, learned confidence gates, Chroma memory, and LLM escalation with Q-learning adaptive calibration to cut LLM calls by 58.78% while reporting 88.68% overall accuracy.