LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Anomaly Detection

arXiv:2310.05668v3 Announce Type: replace
Abstract: Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently. The model trained on old-distribution data is outdated after such changes. Retraining the whole model every time is expensive. Besides, at the beginning of normal pattern changes, there is not enough observation data from the new distribution. Retraining a large neural network model with limited data is vulnerable …

abstract anomaly anomaly detection arxiv change cs.lg current data detection distribution every light normal overfitting patterns retraining services type unsupervised web web services