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.
Survey of intrusion detection systems: techniques, datasets and challenges
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Process mining on the USB-IDS-TC dataset yields low-to-very-high severity ratings for Slowloris DoS alerts while keeping recall at 99.94% and precision at 99.99%.
citing papers explorer
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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.
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Enhancing Anomaly-Based Intrusion Detection Systems with Process Mining
Process mining on the USB-IDS-TC dataset yields low-to-very-high severity ratings for Slowloris DoS alerts while keeping recall at 99.94% and precision at 99.99%.