MULTI-DIMENSIONAL PROFILING OF CYBER THREATS FOR LARGE-SCALE NETWORKS

Abstract

Current multi-domain command and control computer networks require significant oversight to ensure acceptable levels of security. Firewalls are the proactive security management tool at the network’s edge to determine malicious and benign traffic classes. This work aims to develop machine learning algorithms through deep learning and semi-supervised clustering, to enable the profiling of potential threats through network traffic analysis within large-scale networks. This research accomplishes these objectives by analyzing enterprise network data at the packet level using deep learning to classify traffic patterns. In addition, this work examines the efficacy of several machine learning model types and multiple imbalanced data handling techniques. This work also incorporates packet streams for identifying and classifying user behaviors. Tests of the packet classification models demonstrated that deep learning is sensitive to malicious traffic but underperforms in identifying allowed traffic compared to traditional algorithms. However, imbalanced data handling techniques provide performance benefits to some deep learning models. Conversely, semi-supervised clustering accurately identified and classified multiple user behaviors. These models provide an automated tool to learn and predict future traffic patterns. Applying these techniques within large-scale networks detect abnormalities faster and gives network operators greater awareness of user traffic.