Wenbo Guo is a Ph.D. Candidate at Penn State, advised by Professor Xinyu Xing. 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, machine learning, and data mining. 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.

SWS   Info

AG Audience

English

60 Minutes

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 detect concept drifts in security data. Regarding interpretability, I will describe our explanation methods for deep learning-based and deep reinforcement learning-based security systems and demonstrate how security analysts could benefit from these methods to establish trust in blackbox models and patching model vulnerabilities. As for concept drifts, I will introduce a novel ML system to detect and explain drifting samples and demonstrate its application in a real-world malware database. Finally, I will conclude by highlighting my future plan towards maximizing the capability of advanced ML in cybersecurity.

Please contact MPI-SWS Office Team for link information

Susanne Girard

+49 631 9303 9605

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