Infection Categorization Using Deep Autoencoder
Journal
IEEE INFOCOM’18 Demo Session
Date Issued
2018
Author(s)
Abstract
This paper proposes a framework to cluster the infections according to the form of attacking using user and entity behavior analytics. We integrate outside (open-source) and inside (traffic behavior) intelligence and construct a deep autoencoder to develop infection clustering models. According to the evaluation of real infections inside a tier-1 network, we demonstrate the capability of our framework to categorize infections by their intrusion characteristics.
Type
conference paper