在工业质检、安全监控等领域， 多类别无监督异常检测（Multi-class Unsupervised Anomaly Detection, MUAD） 一直是个极具挑战的课题。传统的做法通常是训练一个复杂的编码器-解码器模型，试图重建正常样本的特征。但你有没有想过，这种费时费力的“训练”过程，真的是必须的吗？

Dr. James McCaffrey from Microsoft Research presents a complete program that uses the Python language LightGBM system to create a custom autoencoder for data anomaly detection. You can easily adapt ...

Anomaly detection can be powerful in spotting cyber incidents, but experts say CISOs should balance traditional signature-based detection with more bespoke methods that can identify malicious activity ...