MA-IDS uses two collaborating LLM agents and a persistent experience library to reach 89.75% and 85.22% macro F1 on IoT intrusion datasets while supplying rule-based explanations for each decision.
Quantum machine learning for feature selection in internet of things network intrusion detection,
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MA-IDS: Multi-Agent RAG Framework for IoT Network Intrusion Detection with an Experience Library
MA-IDS uses two collaborating LLM agents and a persistent experience library to reach 89.75% and 85.22% macro F1 on IoT intrusion datasets while supplying rule-based explanations for each decision.