Mulong Luo (Cornell University) and G. Edward Suh (Cornell University)

Effective coordination of sensor inputs requires correct timestamping of the sensor data for robotic vehicles. Though the existing trusted execution environment (TEE) can prevent direct changes to timestamp values from a clock or while stored in memory by an adversary, timestamp integrity can still be compromised by an interrupt between sensor and timestamp reads. We analytically and experimentally evaluate how timestamp integrity violations affect localization of robotic vehicles. The results indicate that the interrupt attack can cause significant errors in localization, which threatens vehicle safety, and need to be prevented with additional countermeasures.

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Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial...

Wei Jia (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Zhaojun Lu (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Haichun Zhang (Huazhong University of Science and Technology), Zhenglin Liu (Huazhong University of Science and Technology), Jie Wang (Shenzhen Kaiyuan Internet Security Co., Ltd), Gang Qu (University…

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Demo #1: Curricular Reinforcement Learning for Robust Policy in...

Yunzhe Tian, Yike Li, Yingxiao Xiang, Wenjia Niu, Endong Tong, and Jiqiang Liu (Beijing Jiaotong University)

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Speeding Dumbo: Pushing Asynchronous BFT Closer to Practice

Bingyong Guo (Institute of Software, Chinese Academy of Sciences), Yuan Lu (Institute of Software Chinese Academy of Sciences), Zhenliang Lu (The University of Sydney), Qiang Tang (The University of Sydney), jing xu (Institute of Software, Chinese Academy of Sciences), Zhenfeng Zhang (Institute of Software, Chinese Academy of Sciences)

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SoK: A Proposal for Incorporating Gamified Cybersecurity Awareness in...

June De La Cruz (INSPIRIT Lab, University of Denver), Sanchari Das (INSPIRIT Lab, University of Denver)

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