A Continuous Re-Authentication Approach Using Ensemble Learning

Abstract

Insider threat becomes a more and more serious problem to organizations. Identify theft is a common attack method among various attack methods from insider. And it is very hard to detection since the attacker acts as a legal identity. Existing researches focus on Human Computer Interaction (HCI) behavior such as keystroke dynamics or mouse dynamics to detection this kind of attack. Based on an obvious observable that different applications show different HCI behavioral patterns (rich-key-operations or rich-mice-operations), we demonstrate that single authentication method is not efficient always in full time work period. In this paper, we provide an ensemble re-authentication approach to detection identify theft in real time, which combine the classification of keystroke-classifier and mice-classifier to determine whether the current operations is produced by the real legitimate user or not. Our experiment proves this new approach is efficient and can provide consistent detection ability with high accuracy.