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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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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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.