  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.
