Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies

arXiv:2405.07440v1 Announce Type: cross
Abstract: Redacted emails satisfy most privacy requirements but they make it more difficult to detect anomalous emails that may be indicative of data exfiltration. In this paper we develop an enhanced method of Active Learning using an information gain maximizing heuristic, and we evaluate its effectiveness in a real world setting where only redacted versions of email could be labeled by human analysts due to privacy concerns. In the first case study we examined how Active …

abstract active learning arxiv cs.cr cs.hc cs.lg data data exfiltration email emails indicative information paper privacy requirements type