Phishing Detection through Email Embeddings
This research addresses the problem of discerning the specific features that contribute to phishing detection for machine learning researchers, by controlling for general indicators.
This paper investigates whether email embeddings can effectively detect phishing emails when general indicators are made similar between phishing and legitimate emails. The study found that email embedding techniques are effective for classifying emails even under these challenging conditions.
The problem of detecting phishing emails through machine learning techniques has been discussed extensively in the literature. Conventional and state-of-the-art machine learning algorithms have demonstrated the possibility of building classifiers with high accuracy. The existing research studies treat phishing and genuine emails through general indicators and thus it is not exactly clear what phishing features are contributing to variations of the classifiers. In this paper, we crafted a set of phishing and legitimate emails with similar indicators in order to investigate whether these cues are captured or disregarded by email embeddings, i.e., vectorizations. We then fed machine learning classifiers with the carefully crafted emails to find out about the performance of email embeddings developed. Our results show that using these indicators, email embeddings techniques is effective for classifying emails as phishing or legitimate.