Piracema.io: A rules-based tree model for phishing prediction

Abstract

Phishing has been consolidating itself as a chronic problem due to its approach to exploiting the end-user, seen as the weakest factor. Through social engineering, the attacker seeks a carelessness of the human being to intercept sensitive data. Concomitantly, the richness in details makes it more difficult to mitigate the attack by most anti-phishing mechanisms since they are sustained in classifying a malicious page that lacks visual and textual details. This study aims to present a rule-based model approach, called piracema.io, for phishing prediction. Compared with other solutions proposed in the literature, the study believes that it has a different model that increases its efficiency in prediction as phishing presents greater richness based on page reputation-driven. In the light of the results obtained in logistic regression, the study detected static and dynamic features, considering relevance, relationship, and similarity between them. As a proof of concept, the study uses a statistical approach to evaluate the prediction modeling over the gradual depth and adherent acting strategies adopted to the proposal. As a result, the study discusses the quantitative and qualitative data obtained by the proposal, presenting contributions, threats, and limitations, as well as perspectives for future work for the continuity and improvement of the model in its current state.