Systems and methods for adaptively managing data drift in a classifier

Abstract

The disclosed computer-implemented method for adaptively managing data drift in a classifier may include (i) receiving, at a computing device, an input sample of digital information having an unknown reputation and (ii) performing a security action that may include (A) identifying the input sample as benign or malicious based on a result obtained by classifying the input sample using a machine learning model trained using activity regularization, (B) calculating an internal activity of the machine learning model occurring during the classifying, (C) calculating an activation entropy of the machine learning model occurring during the classifying, (D) comparing a combination of the internal activity and the activation entropy to a threshold, and (E) when the combination of the internal activity and the activation entropy meets or exceeds the threshold, identifying the result as a low-confidence result. Various other methods, systems, and computer-readable media are also disclosed.