•   When: Thursday, March 03, 2022 from 02:00 PM to 03:00 PM
•   Speakers: Wenbo Guo
•   Location: ZOOM only
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Abstract: 

Nowadays, security researchers are increasingly using AI to automate and facilitate security analysis. Although making some meaningful progress, AI has not maximized its capability in security yet, mainly due to two challenges. First, existing ML techniques have not reached security professionals' requirements in critical properties, such as interpretability and adversary-resistancy. Second, Security data imposes many new technical challenges, and these challenges break the assumptions of existing ML models and thus jeopardize their efficacy. 

 In this talk, I will describe my research efforts to address the above challenges, with a primary focus on strengthening the interpretability of ML-based security systems and enriching ML to handle low-quality labels in security data. I will describe our technique to robustify existing explanation methods against attacks and a novel explanation method for deep learning-based security systems. I will also demonstrate how security analysts could benefit from explanations to discover new knowledge and patch ML model vulnerabilities. Then, I will introduce a novel ML system to enable high accurate categorizations of low-quality attack data and demonstrate its utility in a real-world industrial-level application. Finally, I will conclude by highlighting my plan towards maximizing the capability of advanced ML in cybersecurity.

Bio: 

Wenbo Guo is a Ph.D. Candidate at Penn State and a visiting student at Northwestern. His research interests are machine learning and cybersecurity. His work includes strengthening the fundamental properties of machine learning models and designing customized machine learning models to handle security-unique challenges. He is a recipient of the IBM Ph.D. Fellowship (2020-2022), Facebook/Baidu Ph.D. Fellowship Finalist (2020), and ACM CCS Outstanding Paper Award (2018). His research has been featured by multiple mainstream media and has appeared in a diverse set of top-tier venues in security and machine learning. Going beyond academic research, he also actively participates in many world-class cybersecurity competitions and has won the 2018  DEFCON/GeekPwn AI challenge finalist award.