What is ensemble learning in cybersecurity anomaly detection?

Cybersecurity ensembles combine multiple anomaly detectors to improve intrusion detection accuracy.

Cybersecurity systems face diverse attack patterns, making single-model detection unreliable. Ensembles combine methods like Isolation Forest, autoencoders, clustering, and statistical detectors. Each model captures different anomaly characteristics such as density, reconstruction error, or distance metrics. Aggregation reduces false positives and increases robustness against adversarial attacks.