Jasmin Schwab (German Aerospace Center (DLR)), Alexander Nussbaum (University of the Bundeswehr Munich), Anastasia Sergeeva (University of Luxembourg), Florian Alt (University of the Bundeswehr Munich and Ludwig Maximilian University of Munich), and Verena Distler (Aalto University)

Organizations depend on their employees’ long-term cooperation to help protect the organization from cybersecurity threats. Phishing attacks are the entry point for harmful followup attacks. The acceptance of training measures is thus crucial. Many organizations use simulated phishing campaigns to train employees to adopt secure behaviors. We conducted a preregistered vignette experiment (N=793), investigating the factors that make a simulated phishing campaign seem (un)acceptable, and their influence on employees’ intention to manipulate the campaign. In the experiment, we varied whether employees gave prior consent, whether the phishing email promised a financial incentive and the consequences for employees who clicked on the phishing link. We found that employees’ prior consent positively affected the acceptance of a simulated phishing campaign. The consequences of “employee interview” and “termination of the work contract” negatively affected acceptance. We found no statistically significant effects of consent, monetary incentive, and consequences on manipulation probability. Our results shed light on the factors influencing the acceptance of simulated phishing campaigns. Based on our findings, we recommend that organizations prioritize obtaining informed consent from employees before including them in simulated phishing campaigns and that they clearly describe their consequences. Organizations should carefully evaluate the acceptance of simulated phishing campaigns and consider alternative anti-phishing measures.

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CounterSEVeillance: Performance-Counter Attacks on AMD SEV-SNP

Stefan Gast (Graz University of Technology), Hannes Weissteiner (Graz University of Technology), Robin Leander Schröder (Fraunhofer SIT, Darmstadt, Germany and Fraunhofer Austria, Vienna, Austria), Daniel Gruss (Graz University of Technology)

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Reinforcement Unlearning

Dayong Ye (University of Technology Sydney), Tianqing Zhu (City University of Macau), Congcong Zhu (City University of Macau), Derui Wang (CSIRO’s Data61), Kun Gao (University of Technology Sydney), Zewei Shi (CSIRO’s Data61), Sheng Shen (Torrens University Australia), Wanlei Zhou (City University of Macau), Minhui Xue (CSIRO's Data61)

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CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian...

Kaiyuan Zhang (Purdue University), Siyuan Cheng (Purdue University), Guangyu Shen (Purdue University), Bruno Ribeiro (Purdue University), Shengwei An (Purdue University), Pin-Yu Chen (IBM Research AI), Xiangyu Zhang (Purdue University), Ninghui Li (Purdue University)

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