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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DeGPT: Optimizing Decompiler Output with LLM

Peiwei Hu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Ruigang Liang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, China)

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CP-IoT: A Cross-Platform Monitoring System for Smart Home

Hai Lin (Tsinghua University), Chenglong Li (Tsinghua University), Jiahai Yang (Tsinghua University), Zhiliang Wang (Tsinghua University), Linna Fan (National University of Defense Technology), Chenxin Duan (Tsinghua University)

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Strengthening Privacy in Robust Federated Learning through Secure Aggregation

Tianyue Chu, Devriş İşler (IMDEA Networks Institute & Universidad Carlos III de Madrid), Nikolaos Laoutaris (IMDEA Networks Institute)

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