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Wednesday, February 26 • 3:50pm - 4:15pm
Graph-powered Cyber-Security Intelligence

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Graph-powered Cyber-security intelligence has recently drawn a lot of attention with the emergence of the Cloud era. Cloud applications generate huge volume of logs which are essential for threat detection and investigation. While such massive volume of data enables to accurately detect multiple threats, the data heterogeneity introduced by multiple data sources makes it challenging to analyze data in the tabular data model. Graph data model bridges the limitation by connecting heterogeneous entities via relationships. In this talk, we present how graph technology enables us to build a smarter and deeper cyber-security intelligence. Specifically, we demonstrate how Oracle Labs PGX and Data Studio enable (a) SaaS Cloud Security team to do in-depth analysis of security alerts with visualization and threat hunting from multiple heterogeneous logs, and (b) ODC Moat team to develop an effective Invalid Traffic Detection model by leveraging features extracted from underlying graph.

avatar for Sungpack Hong

Sungpack Hong

Research Director, Oracle
Research Director at Oracle Labs.Leading projects regarding large-scale graph and data analysis -- platforms and applications
avatar for Jinha Kim

Jinha Kim

Principal Member of Technical Staff, Oracle
Jinha Kim is a Principal Member of Technical Staff at Oracle Labs. He is interested in graph analytics and machine learning from designing through implementation to application. He obtained his PhD from Pohang University of Science and Technology, South Korea.

Wednesday February 26, 2020 3:50pm - 4:15pm PST
Bldg 23- Rm 1740 .