Using Transformers for Identification of Persuasion Principles in Phishing Emails
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
It is important to learn about attackers and their attacking strategies so that better and more effective defense systems can be built. During the reconnaissance stage, attackers intend to probe potential targets through various techniques including social engineering attacks. Phishing through email is a well-known, cheap, easy, and surprisingly effective technique for obtaining the needed information. This type of attack targets individuals and thus utilizes weaknesses that might exist in each person. Given the uniqueness of each individual's personality, attackers make sure the right persuasion principle technique is employed for each targeted individual. This paper describes efforts to build machine-learning transformers, the emerging technique in language modeling, with the goal of building classifiers that take into account different types of persuasion principles. More specifically, the paper describes efforts to build machine-learning transformers based on BERT, RoBERTa, and DistilBERT and captures their classification results. The results show that these transformers are accurate enough to build a classification of phishing emails with respect to persuasion techniques. Furthermore, we report that the RoBERTa model is able to train faster than BERT and DistilBERT models.
National Science Foundation
machine-learning transformers, Persuasion principles, phishing attacks
Bimal Karki, Faranak Abri, Akbar Siami Namin, and Keith S. Jones. "Using Transformers for Identification of Persuasion Principles in Phishing Emails" Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (2022): 2841-2848. https://doi.org/10.1109/BigData55660.2022.10020452