Challenges and Opportunities in Security & Privacy in Machine Learning Today's talk : Alfred Chen (University of California, Irvine) Time : 1:00pm Eastern Time Title : On the Semantic AI Security in CPS: The Case of Autonomous Driving Abstract: Abstract: Recent years have witnessed a global phenomenon in the real-world development, testing, deployment, and commercialization of AI-enabled Cyber-Physical Systems (CPSs) such as autonomous driving cars, drones, industrial and home robots. These systems are rapidly revolutionizing a wide range of industries today, from transportation, retail, and logistics (e.g., robo-taxi, autonomous truck, delivery drones/robots), to domotics, manufacturing, construction,and healthcare. In such systems, the AI stacks are in charge of highly safety- and mission-critical decision-making processes such as obstacle avoidance and lane-keeping, which makes their security more critical than ever. Meanwhile, since these AI algorithms are only components of the entire CPS system enclosing them, their security issues are only meaningful when studied with direct integration of the semantic CPS problem context, which forms what we call the “semantic AI security” problem space and introduces various new AI security research challenges. In this talk, I will focus on our recent efforts on the semantic AI security in one of the most safety-critical and fastest-growing AI-enabled CPS today, Autonomous Driving (AD) systems. Specifically, we performed the first security analysis on a wide range of critical AI components in industry-grade AD systems such as 3D perception, sensor fusion, lane detection, localization, prediction, and planning, and in this talk I will describe our key findings and also how we address the corresponding semantic AI security research challenges. I will conclude with a recent systemization of knowledge (SoK) we performed for this growing research space, with a specific emphasis on the most critical scientific gap we observed and our solution proposal. Bio : Alfred Chen is an Assistant Professor of Computer Science at University of California, Irvine. His research interest spans AI security, systems security, and network security. His most recent research focuses are AI security in autonomous driving and intelligent transportation. His works have high impacts in both academic and industry with 30+ research papers in top-tier venues across security, mobile systems, transportation, software engineering, and machine learning; a nationwide USDHS US-CERT alert, multiple CVEs; 50+ news coverage by major media such as Forbes, Fortune, and BBC; and vulnerability report acknowledgments from USDOT, Apple, Microsoft, etc. Recently, his research triggered 30+ autonomous driving companies and the V2X standardization workgroup to start security vulnerability investigations; some confirmed to work on fixes. He co-founded the AutoSec workshop (co-located with NDSS), and co-created DEF CON’s first AutoDriving-themed hacking competition. He received various awards such as NSF CAREER Award, ProQuest Distinguished Dissertation Award, and UCI Chancellor’s Award for mentoring. Chen received Ph.D. from University of Michigan in 2018. Website: [ https://vsehwag.github.io/SPML_seminar/ | https://vsehwag.github.io/SPML_seminar/ ] Mailing list: [ https://groups.google.com/forum/#!forum/spml-seminars/join | Link to mailing list ] Calendar: [ https://calendar.google.com/calendar/u/0?cid=N2FwbTVxYzJsOGM2bXBiNGY4am1oMjN... | Link to calendar ] You can find all additional details on the website. If you are interested, we recommend signing up for the mailing list and sync the calendar to stay up to date with the seminar schedule.