Zhao, Ying

DISA has the needs to improve the Cyber Situational Awareness Analytic Capabilities (CSAAC) and speed of defensive response to the high rate of threats and exploitation for the network.

The current technologies are dominated in Big Data and Deep Learning (BDDL) by systems that provide 1)safe storage, 2) parallel/operational processing, and 3) deep analytics including deep learning for pattern recognition, anomaly detection and data fusion. Deep learning and associated Technogym are the trends of the commercial applications. It is important for CSAAC investing in BDDL now. In this study, we propose to explore how a BDDL framework to automatically monitor and recognize the signatures, patterns and anomalies. We will first identify data sources, design a BDDL ad build a use case to apply the BDDL framework for data fusion, pattern recognition, and anomaly detection.
Information Sciences
Marine Corps Forces Cyberspace Command
Marine Corps
2017