Learn to Adapt: Robust Drift Detection in Security Domain. (arXiv:2206.07581v1 [cs.CR])

Deploying robust machine learning models has to account for concept drifts
arising due to the dynamically changing and non-stationary nature of data.
Addressing drifts is particularly imperative in the security domain due to the
ever-evolving threat landscape and lack of sufficiently labeled training data
at the deployment time leading to performance degradation. Recently proposed
concept drift detection methods in literature tackle this problem by
identifying the changes in feature/data distributions and periodically
retraining the models to learn new concepts. While …

detection domain learn security