Filipo Sharevski (DePaul University), Mattia Mossano, Maxime Fabian Veit, Gunther Schiefer, Melanie Volkamer (Karlsruhe Institute of Technology)

QR codes, designed for convenient access to links, have recently been appropriated as phishing attack vectors. As this type of phishing is relatively and many aspects of the threat in real conditions are unknown, we conducted a study in naturalistic settings (n=42) to explore how people behave around QR codes that might contain phishing links. We found that 28 (67%) of our participants opened the link embedded in the QR code without inspecting the URL for potential phishing cues. As a pretext, we used a poster that invited people to scan a QR code and contribute to a humanitarian aid. The choice of a pretext was persuasive enough that 22 (52%) of our participants indicated that it was the main reason why they scanned the QR code and accessed the embedded link in the first place. We used three link variants to test if people are able to spot a potential phishing threat associated with the poster’s QR code (every participant scanned only one variant). In the variants where the link appeared legitimate or it was obfuscated by a link shortening service, only two out of 26 participants (8%) abandoned the URL when they saw the preview in the QR code scanner app. In the variant when the link explicitly contained the word “phish” in the domain name, this ratio rose to 7 out of 16 participants (44%). We use our findings to propose usable security interventions in QR code scanner apps intended to warn users about potentially phishing links.

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Sticky Fingers: Resilience of Satellite Fingerprinting against Jamming Attacks

Joshua Smailes (University of Oxford), Edd Salkield (University of Oxford), Sebastian Köhler (University of Oxford), Simon Birnbach (University of Oxford), Martin Strohmeier (Cyber-Defence Campus, armasuisse S+T), Ivan Martinovic (University of Oxford)

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Secure Control of Connected and Automated Vehicles Using Trust-Aware...

H M Sabbir Ahmad, Ehsan Sabouni, Akua Dickson (Boston University), Wei Xiao (Massachusetts Institute of Technology), Christos Cassandras, Wenchao Li (Boston University)

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DeepGo: Predictive Directed Greybox Fuzzing

Peihong Lin (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Wei Xie (National University of Defense Technology), Gen Zhang (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions,...

Takami Sato (University of California, Irvine), Yuki Hayakawa (Keio University), Ryo Suzuki (Keio University), Yohsuke Shiiki (Keio University), Kentaro Yoshioka (Keio University), Qi Alfred Chen (University of California, Irvine)

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