Sijie Zhuo (University of Auckland), Robert Biddle (University of Auckland and Carleton University, Ottawa), Lucas Betts, Nalin Asanka Gamagedara Arachchilage, Yun Sing Koh, Danielle Lottridge, Giovanni Russello (University of Auckland)

Phishing is when social engineering is used to deceive a person into sharing sensitive information or downloading malware. Research on phishing susceptibility has focused on personality traits, demographics, and design factors related to the presentation of phishing. There is very little research on how a person’s state of mind might impact outcomes of phishing attacks. We conducted a scenario-based in-lab experiment with 26 participants to examine whether workload affects risky cybersecurity behaviours. Participants were tasked to manage 45 emails for 30 minutes, which included 4 phishing emails. We found that, under high workload, participants had higher physiological arousal and longer fixations, and spent half as much time reading email compared to low workload. There was no main effect for workload on phishing clicking, however a post-hoc analysis revealed that participants were more likely to click on task-relevant phishing emails compared to non-relevant phishing emails during high workload whereas there was no difference during low workload. We discuss the implications of state of mind and attention related to risky cybersecurity behaviour.

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From Hardware Fingerprint to Access Token: Enhancing the Authentication...

Yue Xiao (Wuhan University), Yi He (Tsinghua University), Xiaoli Zhang (Zhejiang University of Technology), Qian Wang (Wuhan University), Renjie Xie (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Qi Li (Tsinghua University)

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Powers of Tau in Asynchrony

Sourav Das (University of Illinois at Urbana-Champaign), Zhuolun Xiang (Aptos), Ling Ren (University of Illinois at Urbana-Champaign)

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Towards Precise Reporting of Cryptographic Misuses

Yikang Chen (The Chinese University of Hong Kong), Yibo Liu (Arizona State University), Ka Lok Wu (The Chinese University of Hong Kong), Duc V Le (Visa Research), Sze Yiu Chau (The Chinese University of Hong Kong)

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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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