# Speaker: Shiqing Ma (Rutgers University)

# Title: A Provenance Approach to Improve Modern System Transparency

# Abstract: Modern computing systems act as black boxes in that they provide little to no visibility of their internal workings. However, the transparency of modern computing systems is critical for system security and problem diagnosis. For example, advanced persistent threats (APTs) are highly sophisticated, multi-staged attacks that act slowly and deliberately over a long period of time to expand their presence in an enterprise environment and reach their goals. However, the opaqueness of enterprise systems makes it difficult to infer causality between operations (e.g., sensitive files being stolen and visiting a malicious website a few weeks ago) so that attacks cannot be understood and defended. A natural solution to this problem is leverage the provenance of system objects and subjects. In this talk, I will discuss my work on provenance based solutions to improve system operation transparency for security purposes with a focus on building an effective and efficient infrastructure for collecting and processing provenance data.

# Bio: Shiqing Ma is an Assistant Professor in the Department of Computer Science at Rutgers University, the state university of New Jersey. He received his Ph.D. in Computer Science from Purdue University in 2019. His research focuses on program analysis, software and system security, adversarial machine learning and software engineering. He is the recipient of Distinguished Paper Awards from NDSS 2016 and USENIX Security 2017.