An AI agent for ACMIS uses supervised anomaly detection, behavioral analytics, and an NLP chatbot to report 0.966 macro F1 on simulated threat data, outperforming rule-based and LSTM baselines.
Adversarial examples: at- tacks and defenses for deep learning,
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An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
An AI agent for ACMIS uses supervised anomaly detection, behavioral analytics, and an NLP chatbot to report 0.966 macro F1 on simulated threat data, outperforming rule-based and LSTM baselines.