Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails. (arXiv:2303.00870v1 [cs.HC])

Research on email anomaly detection has typically relied on specially
prepared datasets that may not adequately reflect the type of data that occurs
in industry settings. In our research, at a major financial services company,
privacy concerns prevented inspection of the bodies of emails and attachment
details (although subject headings and attachment filenames were available).
This made labeling possible anomalies in the resulting redacted emails more
difficult. Another source of difficulty is the high volume of emails combined
with the …

anomaly detection attachment cybersecurity data datasets detection email emails financial financial services industry labeling major may privacy research services settings