DATE is pleased to present a special hybrid format for its 2022 event, as the situation related to COVID-19 is improving but safety measures and restrictions will remain uncertain for the upcoming months across Europe and worldwide. In transition towards a future post-pandemic event again, DATE 2022 will host a two-day live event in presence in the city of Antwerp (just north of Brussels in Belgium), to bring the community together again, followed by other activities carried out entirely online in the subsequent days. This setup combines the in-presence experience with the opportunities of on-line activities, fostering the networking and social interactions around an interesting program of selected talks and panels on emerging topics to complement the traditional DATE high-quality scientific, technical and educational activities.

M05 Automation goes both ways: ML for security and security for ML

Monday, 1 February 2021 15:00
Monday, 1 February 2021 18:40
Alexandra Dimitrenko, University of Würzburg, United States
Siddarth Garg, New York University, United States
Farinaz Koushanfar, University of California San Diego, United States

This tutorial focuses on the state of the art research in the intersection of AI and security. On the one hand, recent advances in Deep Learning (DL) have enabled a paradigm shift to include machine intelligence in a wide range of autonomous tasks. As a result, a largely unexplored surface has opened up for attacks jeopardizing the integrity of DL models and hindering their ubiquitous deployment across various intelligent applications. On the other hand, DL-based algorithms are also being employed for identifying several security vulnerabilities on long streams of multi-modal data and logs. In distributed complex settings, often times this is the only way to monitor and audit the security and robustness of the system. The tutorial integrates the views from three experts: Prof. Garg explores the emerging landscape of "adversarial ML" with the goal of answering basic questions about the trustworthiness and reliability of modern machine learning systems. Prof. Dmitrienko presents novel usages of federated and distributed learning for risk detection on mobile platforms with proof-of-concept realization and evaluation on data from millions of users. Prof. Koushanfar discusses how end-to-end automated frameworks based on algorithm/hardware co-design help with both (1) realizing accelerated low-overhead shields against DL attacks, and (2) enabling low overhead and real-time intelligent security monitoring.