beat365系列講座菁英論壇第35期——The Road Towards Accurate, Scalable and Robust Graph-based Security Analytics: Where Are We Now?

報告題目(Title)：The Road Towards Accurate, Scalable and Robust Graph-based Security Analytics: Where Are We Now?

時間(Date & Time)：2024.9.13     10:00am-11:30am

地點(Location)：理科一号樓1504（燕園校區）Room 1504, Science Building #1 (Yanyuan)

主講人(Speaker)：Zhou Li

邀請人(Host)：李錠

報告摘要(Abstract)：

Graph learning has gained prominent traction from the academia and industry as a solution to detect complex cyber-attack campaigns. By constructing a graph that connects various network/host entities and modeling the benign/malicious patterns, threat-hunting tasks like data provenance and entity classification can automated. We term the systems under this theme as Graph-based Security Analytics (GSAs). In this talk, we first provide a cursory view of GSA research in the recent decade, focusing on the academic side. Then, we elaborate a few GSAs developed in our lab, which are designed for edge-level intrusion detection (Argus), subgraph-level attack reconstruction (ProGrapher) and storage reduction (SEAL). In the end of the talk, we will review the progress and pitfalls along the development of GSA research, and highlight some research opportunities.

主講人簡介(Bio)：

Zhou Li is an Assistant Professor at UC Irvine, EECS department, leading the Data-driven Security and Privacy Lab. Before joining UC Irvine, he worked as Principal Research Scientist at RSA Labs from 2014 to 2018. His research interests include Internet Security, Organizational network security, Privacy Enhancement Technologies, and Security and privacy for machine learning. He received the NSF CAREER award, Amazon Research Award, Microsoft Security AI award and IRTF Applied Networking Research Prize. 

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