Mohd Sabra (University of Texas at San Antonio), Anindya Maiti (University of Oklahoma), Murtuza Jadliwala (University of Texas at San Antonio)

Due to recent world events, video calls have become the new norm for both personal and professional remote communication. However, if a participant in a video call is not careful, he/she can reveal his/her private information to others in the call. In this paper, we design and evaluate an attack framework to infer one type of such private information from the video stream of a call -- keystrokes, i.e., text typed during the call. We evaluate our video-based keystroke inference framework using different experimental settings, such as different webcams, video resolutions, keyboards, clothing, and backgrounds. Our high keystroke inference accuracies under commonly occurring experimental settings highlight the need for awareness and countermeasures against such attacks. Consequently, we also propose and evaluate effective mitigation techniques that can automatically protect users when they type during a video call.

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XDA: Accurate, Robust Disassembly with Transfer Learning

Kexin Pei (Columbia University), Jonas Guan (University of Toronto), David Williams-King (Columbia University), Junfeng Yang (Columbia University), Suman Jana (Columbia University)

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An Analysis of First-Party Cookie Exfiltration due to CNAME...

Tongwei Ren (Worcester Polytechnic Institute), Alexander Wittmany (University of Kansas), Lorenzo De Carli (Worcester Polytechnic Institute), Drew Davidsony (University of Kansas)

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Does Every Second Count? Time-based Evolution of Malware Behavior...

Alexander Küchler (Fraunhofer AISEC), Alessandro Mantovani (EURECOM), Yufei Han (NortonLifeLock Research Group), Leyla Bilge (NortonLifeLock Research Group), Davide Balzarotti (EURECOM)

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Time-Based CAN Intrusion Detection Benchmark

Deborah Blevins (University of Kentucky), Pablo Moriano, Robert Bridges, Miki Verma, Michael Iannacone, and Samuel Hollifield (Oak Ridge National Laboratory)

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