  Concept drift is a significant challenge for malware detection, as the
performance of trained machine learning models degrades over time, rendering
them impractical. While prior research in malware concept drift adaptation has
primarily focused on active learning, which involves selecting representative
samples to update the model, self-training has emerged as a promising approach
to mitigate concept drift. Self-training involves retraining the model using
pseudo labels to adapt to shifting data distributions. In this research, we
propose MORPH -- an effective pseudo-label-based concept drift adaptation
method specifically designed for neural networks. Through extensive
experimental analysis of Android and Windows malware datasets, we demonstrate
the efficacy of our approach in mitigating the impact of concept drift. Our
method offers the advantage of reducing annotation efforts when combined with
active learning. Furthermore, our method significantly improves over existing
works in automated concept drift adaptation for malware detection.
