Phishing

A novel framework designed to designed to extract contextual information from phishing emails. Our focus is on human-centric features, which are often overlooked in traditional approaches, as they are the features that users notice when evaluating a potentially suspicious email. By fine-tuning four transformer-based models, we accurately extract seven descriptive features from phishing email texts. Our findings indicate that language models provide a promising method for extracting contextual information from phishing emails.

There is a significant knowledge gap about the qualitative traits embedded in phishing emails, which could be useful in a range of phishing mitigation tasks. In this paper, we consider the structure of phishing emails to identify a novel set of descriptive features. We employ an iterative qualitative coding approach to identify features that are descriptive of the emails leading to the ``Phishing Codebook’’, a structured framework for extracting key phishing email information. We apply this codebook to a publicly available dataset of 503 phishing emails.

Attackers attempt to create successful phishing campaigns by sending out trustworthy-looking emails with a range of variations, such as …

When people report phishing emails they sometimes say something along with the report, such as asking a question, highlighting an aspect of the phishing, or asking for guidance. In this work we looked at 270 phishing reports and analyzed what people said alongside the report. We find that people often present a case for why they think this email is phishing or why it is particularly harmful and deserves attention.

Common anti-phishing advice tells users to mouse over links, look at the URL, and compare to the expected destination, implicitly …