Infection categorization using deep autoencoder
Ming-Hung Wang, Meng-Han Tsai, Wei-Chieh Yang, Chin‐Laung Lei · 2018
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.