Currently, there are many ways that phishing attacks placed people and businesses at risk. In the economic causes losses the money; in the social aspect, the perception of trust in users decreases; and on the psychological level, fear can avoid the use of digital tools and resources. This study aims to increase efficiency in detecting Phishing attacks using Natural Language Processing (NLP) to explore the mental model people use to detect whether an email is legitimate or not. Specifically, it is based on feedback vectorization and the movement of the mouse, which was obtained when the participants interacted in a test to detect phishing. The results obtained allow us to identify that people based their decision on the URL analysis in the mental model of legitimation and phishing decision. However, in the phishing model, the number of characteristics in each indicator would be more diverse and broader, which produces new challenges and future directions in this solution.
|Title of host publication||Proceedings of the 2021 5th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - 29 Jul 2021|
|Event||5th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2021 - London, United Kingdom|
Duration: 29 Jul 2021 → 30 Jul 2021
|Name||Proceedings of the 2021 5th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2021|
|Conference||5th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2021|
|Period||29/07/21 → 30/07/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS We want to thank the resources granted for developing the research project entitled “Detection and Mitigation of Social Engineering attacks applying Cognitive Security, Code: PIC- ESPE-2020-Social Engineering. Likewise, we would like to thank the financial resources provided by the Universidad Politécnica Salesiana of Quito, Ecuador.
© 2021 IEEE.
- Mental model
- Natural language processing