Affiliation:
1. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
2. School of Computer Science, Beijing University of Technology, Beijing 100124, China
Abstract
While antiphishing techniques have evolved over the years, phishing remains one of the most threatening attacks on current network security. This is because phishing exploits one of the weakest links in a network system—people. The purpose of this research is to predict the possible phishing victims. In this study, we propose the multidimensional phishing susceptibility prediction model (MPSPM) to implement the prediction of user phishing susceptibility. We constructed two types of emails: legitimate emails and phishing emails. We gathered 1105 volunteers to join our experiment by recruiting volunteers. We sent these emails to volunteers and collected their demographic, personality, knowledge experience, security behavior, and cognitive processes by means of a questionnaire. We then applied 7 supervised learning methods to classify these volunteers into two categories using multidimensional features: susceptible and nonsusceptible. The experimental results indicated that some machine learning methods have high accuracy in predicting user phishing susceptibility, with a maximum accuracy rate of 89.04%. We conclude our study with a discussion of our findings and their future implications.
Funder
National Basic Research Program of China
Subject
General Mathematics,General Medicine,General Neuroscience,General Computer Science
Cited by
8 articles.
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