Wentao Chen, Sam Der, Yunpeng Luo, Fayzah Alshammari, Qi Alfred Chen (University of California, Irvine)

Due to the cyber-physical nature of robotic vehicles, security is especially crucial, as a compromised system not only exposes privacy and information leakage risks, but also increases the risk of harm in the physical world. As such, in this paper, we explore the current vulnerability landscape of robotic vehicles exposed to and thus remotely accessible by any party on the public Internet. Focusing particularly on instances of the Robot Operating System (ROS), a commonly used open-source robotic software framework, we performed new Internet-wide scans of the entire IPv4 address space, identifying, categorizing, and analyzing the ROS-based systems we discovered. We further performed the first measurement of ROS scanners in the wild by setting up ROS honeypots, logging traffic, and analyzing the traffic we received. We found over 190 ROS systems on average being regularly exposed to the public Internet and discovered new trends in the exposure of different types of robotic vehicles, suggesting increasing concern regarding the cybersecurity of today’s ROS-based robotic vehicle systems.

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TALISMAN: Tamper Analysis for Reference Monitors

Frank Capobianco (The Pennsylvania State University), Quan Zhou (The Pennsylvania State University), Aditya Basu (The Pennsylvania State University), Trent Jaeger (The Pennsylvania State University, University of California, Riverside), Danfeng Zhang (The Pennsylvania State University, Duke University)

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BreakSPF: How Shared Infrastructures Magnify SPF Vulnerabilities Across the...

Chuhan Wang (Tsinghua University), Yasuhiro Kuranaga (Tsinghua University), Yihang Wang (Tsinghua University), Mingming Zhang (Zhongguancun Laboratory), Linkai Zheng (Tsinghua University), Xiang Li (Tsinghua University), Jianjun Chen (Tsinghua University; Zhongguancun Laboratory), Haixin Duan (Tsinghua University; Quan Cheng Lab; Zhongguancun Laboratory), Yanzhong Lin (Coremail Technology Co. Ltd), Qingfeng Pan (Coremail Technology Co. Ltd)

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Timing Channels in Adaptive Neural Networks

Ayomide Akinsanya (Stevens Institute of Technology), Tegan Brennan (Stevens Institute of Technology)

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