Prediction of Phishing Susceptibility Based on a Combination of Static and Dynamic Features

Rundong Yang, Kangfeng Zheng, Bin Wu, Chunhua Wu, Xiujuan Wang and Man Fai Leung

Mathematical Problems in Engineering, 2022, vol. 2022, 1-10

Abstract: Phishing is a very serious security problem that poses a huge threat to the average user. Research on phishing prevention is attracting increasing attention. The root cause of the threat of phishing is that phishing can still succeed even when anti-phishing tools are utilized, which is due to the inability of users to correctly identify phishing attacks. Current research on phishing focuses on examining the static characteristics of the phishing behavior phenomenon, which cannot truly predict a user’s susceptibility to phishing. In this paper, a user phishing susceptibility prediction model (DSM) that is based on a combination of dynamic and static features is proposed. The model investigates how the user’s static feature factors (experience, demographics, and knowledge) and dynamic feature factors (design changes and eye tracking) affect susceptibility. A hybrid Long Short-Term Memory (LSTM) and LightGBM prediction model is designed to predict user susceptibility. Finally, we evaluate the prediction performance of the DSM by conducting a questionnaire survey of 1150 volunteers and an eye-tracking experiment on 50 volunteers. According to the experimental results, the correct prediction rate of the DSM is higher than that for individual feature prediction, which reached 92.34%. These research experiments demonstrate the effectiveness of the DSM in predicting users’ susceptibility to phishing using a combination of static and dynamic features.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/2884769.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/2884769.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2884769

DOI: 10.1155/2022/2884769

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().