Dongyao Chen (Shanghai Jiao Tong University), Mert D. Pesé (Clemson University), Kang G. Shin (University of Michigan, Ann Arbor)

ZOOX Best Paper Award Winner ($500 cash prize)!

Driving apps, such as navigation, fuel-price, and road services, have been deployed and used widely. The car-related nature of these services may motivate them to infer the type of their users’ vehicles. We first apply systematic analytics on real-world apps to show that the vehicle-type — seemingly unharmful — information may have serious privacy implications. Next, we demonstrate that attackers can harvest the features of these mobile apps to infer the car-type information in a stealthy way. Specifically, we explore the use of zero-permission mobile motion sensors to extract spectral features for differentiating the engines and body types of vehicles. Based on our experimental results of 17 different cars, we have achieved 82+% and 85+% overall accuracy in identifying three major engine types and four popular body types, respectively.

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No Grammar, No Problem: Towards Fuzzing the Linux Kernel...

Alexander Bulekov (Boston University), Bandan Das (Red Hat), Stefan Hajnoczi (Red Hat), Manuel Egele (Boston University)

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AutoWatch: Learning Driver Behavior with Graphs for Auto Theft...

Paul Agbaje, Abraham Mookhoek, Afia Anjum, Arkajyoti Mitra (University of Texas at Arlington), Mert D. Pesé (Clemson University), Habeeb Olufowobi (University of Texas at Arlington)

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Investigating User Behaviour Towards Fake News on Social Media...

Yasmeen Abdrabou (University of the Bundeswehr Munich), Elisaveta Karypidou (LMU Munich), Florian Alt (University of the Bundeswehr Munich), Mariam Hassib (University of the Bundeswehr Munich)

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