Real-Time, Evidence-Based Alerts for Protection From Phishing Attacks

Shahryar Baki, Fatima Zahra Qachfar, Rakesh M. Verma, Ryan Kennedy, Daniel Jones

Abstract: Despite two decades of research on automatic filtering systems, phishing attacks remain a serious problem. To alleviate risks from filtering failures, we design and evaluate the effectiveness of a new warning system on users’ susceptibility to phishing. Our proposed technique highlights key sentences based on an analysis of the persuasive techniques used. An online mixed-design study ($n=604$) shows that adding our highlighting technique outperforms existing warning solutions. It also identifies the relative efficacy of different appeals and the characteristics of susceptible users. Results show that adding our highlighting technique is useful even with false positives and false negatives. Inspired by this result, we propose an automatic warning generator. We created a small labeled dataset of suspicious sentences and used data augmentation. Our best models achieve F1 score of 99.95% in detecting phishing emails and 88% in detecting suspicious sentences.

External IDs:dblp:journals/tdsc/BakiQVKJ25