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Phishing to improve detection

Zheng, Sarah Y; Becker, Ingolf; (2023) Phishing to improve detection. In: Proceedings of the 2023 European Symposium on Usable Security (EuroUSEC 2023). ACM (Association for Computing Machinery) (In press). Green open access

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Abstract

Phishing e-mail scams continue to threaten organisations around the world. With generative artificial intelligence, conventional phishing detection advice such as looking out for linguistic errors and bad layouts will become obsolete. New approaches to improve people’s ability to detect phishing are essential. We report on promising results from two experiments (total N = 183) that engaging people with an adversarial mindset improves their ability to detect phishing e-mails compared to those who received conventional or no training. Participants who completed conventional training were nearly three times as likely to fall for a simulated phishing attack compared to those who completed the adversarial training, in which they watched a fictitious cybercriminal explain how to devise a targeted phishing e-mail, and then wrote targeted phishing e-mails themselves. Although further research is needed to examine the training’s long-term efficacy with larger sample sizes, the present findings show an encouraging alternative to conventional phishing training approaches.

Type: Proceedings paper
Title: Phishing to improve detection
Event: The 2023 European Symposium on Usable Security (EuroUSEC 2023)
Location: Copenhagen, Denmar
Dates: 16th-17th October 2023
ISBN-13: 979-8-4007-0814-5/23/10
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3617072.3617121
Publisher version: https://dl.acm.org/conference/eurousec
Language: English
Additional information: © The Authors 2023. Original content in this paper is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
Keywords: phishing detection, cybersecurity training, adversarial mindset
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10175677
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