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 these types of strategies should absolutely be conducted when possible, they are not robust towards attacker-induced drifts and suffer from a delay in detecting new attacks. We aim to address these shortcomings in this work. we propose a robust drift detector that not only identifies drifted samples but also discovers new classes as they arrive in an on-line fashion. We evaluate the proposed method with two security-relevant data sets -- network intrusion data set released in 2018 and APT Command and Control dataset combined with web categorization data. Our evaluation shows that our drifting detection method is not only highly accurate but also robust towards adversarial drifts and discovers new classes from drifted samples.