A semi-supervised temporal framework for cloud network intrusion detection that combines supervised learning with consistency regularization and selective pseudo-labeling to improve robustness against adversarial contamination and temporal drift.
ASurvey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
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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.
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.
citing papers explorer
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Robust Semi-Supervised Temporal Intrusion Detection for Adversarial Cloud Networks
A semi-supervised temporal framework for cloud network intrusion detection that combines supervised learning with consistency regularization and selective pseudo-labeling to improve robustness against adversarial contamination and temporal drift.
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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.
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AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.