Approaches to adversarial drift
Alex Kantchelian, Sadia Afroz, Ling Huang, Aylin Caliskan Islam, Brad Miller, Michael Carl Tschantz, Rachel Greenstadt, Anthony Douglas Joseph, J. D. Tygar · 2013
In this position paper, we argue that to be of practical interest, a machine-learning based security system must engage with the human operators beyond feature engineering and instance labeling to address the challenge of drift in adversarial environments. We propose that designers of such systems broaden the classification goal into an explanatory goal, which would deepen the interaction with system's operators.