Impact of prior victimization on susceptibility to phishing attacks
Authors
Pawar, Kajal
Issue Date
2024-05
Degree
MSc in Business Analytics
Publisher
Dublin Business School
Rights holder
Rights
Abstract
This study evaluated various machine learning (ML) algorithms to predict susceptibility to phishing attacks based on prior victimization. The models assessed included Decision Tree Classifier, Support Vector Classifier (SVC), Random Forest, XGBoost, and Logistic Regression. SVC and Logistic Regression achieved the highest accuracy of 0.86 and an F1 score of 0.79, making them top performers. Random Forest also showed strong results with an accuracy of 0.85 and an F1 score of 0.79, while XGBoost had an accuracy of 0.82 and an F1 score of 0.79. The Decision Tree Classifier was the least effective, with an accuracy of 0.75 and an F1 score of 0.76. Feature selection significantly enhanced model performance, and the
quality and size of the training dataset were crucial. This study concludes that SVC and Logistic Regression are the most effective models for predicting phishing susceptibility, offering valuable insights for improving cybersecurity measures.