Zhisheng Hu (Baidu Security), Junjie Shen (UC Irvine), Shengjian Guo (Baidu Security), Xinyang Zhang (Baidu Security), Zhenyu Zhong (Baidu Security), Qi Alfred Chen (UC Irvine) and Kang Li (Baidu Security)

Safety and security play critical roles for the success of Autonomous Driving (AD) systems. Since AD systems heavily rely on AI components, the safety and security research of such components has also received great attention in recent years. While it is widely recognized that AI component-level (mis)behavior does not necessarily lead to AD system-level impacts, most of existing work still only adopts component-level evaluation. To fill such critical scientific methodology-level gap from component-level to real system-level impact, a system-driven evaluation platform jointly constructed by the community could be the solution. In this paper, we present PASS (Platform for Auto-driving Safety and Security), a system-driven evaluation prototype based on simulation. By sharing our platform building concept and preliminary efforts, we hope to call on the community to build a uniform and extensible platform to make AI safety and security work sufficiently meaningful at the system level.

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Repttack: Exploiting Cloud Schedulers to Guide Co-Location Attacks

Chongzhou Fang (University of California, Davis), Han Wang (University of California, Davis), Najmeh Nazari (University of California, Davis), Behnam Omidi (George Mason University), Avesta Sasan (University of California, Davis), Khaled N. Khasawneh (George Mason University), Setareh Rafatirad (University of California, Davis), Houman Homayoun (University of California, Davis)

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VISAS-Detecting GPS spoofing attacks against drones by analyzing camera's...

Barak Davidovich (Ben-Gurion University of the Negev), Ben Nassi (Ben-Gurion University of the Negev) and Yuval Elovici (Ben-Gurion University of the Negev)

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ditto: WAN Traffic Obfuscation at Line Rate

Roland Meier (ETH Zürich), Vincent Lenders (armasuisse), Laurent Vanbever (ETH Zürich)

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