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|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Avoiding the hook : influential factors of phishing awareness training on click-rates and a data-driven approach to predict email difficulty perception|
Bozkir, Ahmet Selman
|Published in:||IEEE Access|
|Publisher / Ed. Institution:||IEEE|
|Subjects:||Machine learning; Phishing; Phishing awareness; Human factor; Predictive model; Information security; Human computer interaction; Difficulty estimation|
|Subject (DDC):||005: Computer programming, programs and data |
|Abstract:||Phishing attacks are still seen as a significant threat to cyber security, and large parts of the industry rely on anti-phishing simulations to minimize the risk imposed by such attacks. This study conducted a large-scale anti-phishing training with more than 31000 participants and 144 different simulated phishing attacks to develop a data-driven model to classify how users would perceive a phishing simulation. Furthermore, we analyze the results of our large-scale anti-phishing training and give novel insights into users’ click behavior. Analyzing our anti-phishing training data, we find out that 66% of users do not fall victim to credential-based phishing attacks even after being exposed to twelve weeks of phishing simulations. To further enhance the phishing awareness-training effectiveness, we developed a novel manifold learning-powered machine learning model that can predict how many people would fall for a phishing simulation using the several structural and state-of-the-art NLP features extracted from the emails. In this way, we present a systematic approach for the training implementers to estimate the average “convincing power” of the emails prior to rolling out. Moreover, we revealed the top-most vital factors in the classification. In addition, our model presents significant benefits over traditional rule-based approaches in classifying the difficulty of phishing simulations. Our results clearly show that anti-phishing training should focus on the training of individual users rather than on large user groups. Additionally, we present a promising generic machine learning model for predicting phishing susceptibility.|
|Fulltext version:||Published version|
|License (according to publishing contract):||CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Applied Information Technology (InIT)|
|Published as part of the ZHAW project:||OptiPhish – Effective and Measurable Phishing Awareness Training|
|Appears in collections:||Publikationen School of Engineering|
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|2022_Sutter-etal_Influential-factors-of-Phishing-awareness-training.pdf||4.28 MB||Adobe PDF|
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Sutter, T., Bozkir, A. S., Gehring, B., & Berlich, P. (2022). Avoiding the hook : influential factors of phishing awareness training on click-rates and a data-driven approach to predict email difficulty perception. IEEE Access, 10, 100540–100565. https://doi.org/10.1109/ACCESS.2022.3207272
Sutter, T. et al. (2022) ‘Avoiding the hook : influential factors of phishing awareness training on click-rates and a data-driven approach to predict email difficulty perception’, IEEE Access, 10, pp. 100540–100565. Available at: https://doi.org/10.1109/ACCESS.2022.3207272.
T. Sutter, A. S. Bozkir, B. Gehring, and P. Berlich, “Avoiding the hook : influential factors of phishing awareness training on click-rates and a data-driven approach to predict email difficulty perception,” IEEE Access, vol. 10, pp. 100540–100565, Sep. 2022, doi: 10.1109/ACCESS.2022.3207272.
Sutter, Thomas, et al. “Avoiding the Hook : Influential Factors of Phishing Awareness Training on Click-Rates and a Data-Driven Approach to Predict Email Difficulty Perception.” IEEE Access, vol. 10, Sept. 2022, pp. 100540–65, https://doi.org/10.1109/ACCESS.2022.3207272.
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