Investigating Labelless Drift Adaptation for Malware Detection
Zeliang Kan, Feargus Pendlebury, Fabio Pierazzi, Lorenzo Cavallaro · 2021
The evolution of malware has long plagued machine learning-based detection systems, as malware authors develop innovative strategies to evade detection and chase profits. This induces concept drift as the test distribution diverges from the training, causing performance decay that requires constant monitoring and adaptation.